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Data Science Crash Course Skin Cancer Classification And Prediction Using Machine Learning And Deep Learning
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Book Synopsis DATA SCIENCE CRASH COURSE: Skin Cancer Classification and Prediction Using Machine Learning and Deep Learning by : Vivian Siahaan
Download or read book DATA SCIENCE CRASH COURSE: Skin Cancer Classification and Prediction Using Machine Learning and Deep Learning written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2022-02-01 with total page 85 pages. Available in PDF, EPUB and Kindle. Book excerpt: Skin cancer develops primarily on areas of sun-exposed skin, including the scalp, face, lips, ears, neck, chest, arms and hands, and on the legs in women. But it can also form on areas that rarely see the light of day — your palms, beneath your fingernails or toenails, and your genital area. Skin cancer affects people of all skin tones, including those with darker complexions. When melanoma occurs in people with dark skin tones, it's more likely to occur in areas not normally exposed to the sun, such as the palms of the hands and soles of the feet. Dataset used in this project contains a balanced dataset of images of benign skin moles and malignant skin moles. The data consists of two folders with each 1800 pictures (224x244) of the two types of moles. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. The deep learning models used are CNN and MobileNet.
Book Synopsis Classification and Prediction Projects with Machine Learning and Deep Learning by : Vivian Siahaan
Download or read book Classification and Prediction Projects with Machine Learning and Deep Learning written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2022-02-06 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: PROJECT 1: DATA SCIENCE CRASH COURSE: Drinking Water Potability Classification and Prediction Using Machine Learning and Deep Learning with Python Access to safe drinking water is essential to health, a basic human right, and a component of effective policy for health protection. This is important as a health and development issue at a national, regional, and local level. In some regions, it has been shown that investments in water supply and sanitation can yield a net economic benefit, since the reductions in adverse health effects and health care costs outweigh the costs of undertaking the interventions. The drinkingwaterpotability.csv file contains water quality metrics for 3276 different water bodies. The columns in the file are as follows: ph, Hardness, Solids, Chloramines, Sulfate, Conductivity, Organic_carbon, Trihalomethanes, Turbidity, and Potability. Contaminated water and poor sanitation are linked to the transmission of diseases such as cholera, diarrhea, dysentery, hepatitis A, typhoid, and polio. Absent, inadequate, or inappropriately managed water and sanitation services expose individuals to preventable health risks. This is particularly the case in health care facilities where both patients and staff are placed at additional risk of infection and disease when water, sanitation, and hygiene services are lacking. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 2: DATA SCIENCE CRASH COURSE: Skin Cancer Classification and Prediction Using Machine Learning and Deep Learning Skin cancer develops primarily on areas of sun-exposed skin, including the scalp, face, lips, ears, neck, chest, arms and hands, and on the legs in women. But it can also form on areas that rarely see the light of day — your palms, beneath your fingernails or toenails, and your genital area. Skin cancer affects people of all skin tones, including those with darker complexions. When melanoma occurs in people with dark skin tones, it's more likely to occur in areas not normally exposed to the sun, such as the palms of the hands and soles of the feet. Dataset used in this project contains a balanced dataset of images of benign skin moles and malignant skin moles. The data consists of two folders with each 1800 pictures (224x244) of the two types of moles. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. The deep learning models used are CNN and MobileNet.
Book Synopsis DATA SCIENCE CRASH COURSE: Thyroid Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI by : Vivian Siahaan
Download or read book DATA SCIENCE CRASH COURSE: Thyroid Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-07-17 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: Thyroid disease is a prevalent condition that affects the thyroid gland, leading to various health issues. In this session of the Data Science Crash Course, we will explore the classification and prediction of thyroid disease using machine learning and deep learning techniques, all implemented with the power of Python and a user-friendly GUI built with PyQt. We will start by conducting data exploration on a comprehensive dataset containing relevant features and thyroid disease labels. Through analysis and pattern recognition, we will gain insights into the underlying factors contributing to thyroid disease. Next, we will delve into the machine learning phase, where we will implement popular algorithms including Support Vector, Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Gradient Boosting, Light Gradient Boosting, Naive Bayes, Adaboost, Extreme Gradient Boosting, and Multi-Layer Perceptron. These models will be trained using different preprocessing techniques, including raw data, normalization, and standardization, to evaluate their performance and accuracy. We train each model on the training dataset and evaluate its performance using appropriate metrics such as accuracy, precision, recall, and F1-score. This helps us assess how well the models can predict stroke based on the given features. To optimize the models' performance, we perform hyperparameter tuning using techniques like grid search or randomized search. This involves systematically exploring different combinations of hyperparameters to find the best configuration for each model. After training and tuning the models, we save them to disk using joblib. This allows us to reuse the trained models for future predictions without having to train them again. Moving beyond traditional machine learning, we will build an artificial neural network (ANN) using TensorFlow. This ANN will capture complex relationships within the data and provide accurate predictions of thyroid disease. To ensure the effectiveness of our ANN, we will train it using a curated dataset split into training and testing sets. This will allow us to evaluate the model's performance and its ability to generalize predictions. To provide an interactive and user-friendly experience, we will develop a Graphical User Interface (GUI) using PyQt. The GUI will allow users to input data, select prediction methods (machine learning or deep learning), and visualize the results. Through the GUI, users can explore different prediction methods, compare performance, and gain insights into thyroid disease classification. Visualizations of training and validation loss, accuracy, and confusion matrices will enhance understanding and model evaluation. Line plots comparing true values and predicted values will further aid interpretation and insights into classification outcomes. Throughout the project, we will emphasize the importance of preprocessing techniques, feature selection, and model evaluation in building reliable and effective thyroid disease classification and prediction models. By the end of the project, readers will have gained practical knowledge in data exploration, machine learning, deep learning, and GUI development. They will be equipped to apply these techniques to other domains and real-world challenges. The project’s comprehensive approach, from data exploration to model development and GUI implementation, ensures a holistic understanding of thyroid disease classification and prediction. It empowers readers to explore applications of data science in healthcare and beyond. The combination of machine learning and deep learning techniques, coupled with the intuitive GUI, offers a powerful framework for thyroid disease classification and prediction. This project serves as a stepping stone for readers to contribute to the field of medical data science. Data-driven approaches in healthcare have the potential to unlock valuable insights and improve outcomes. The focus on thyroid disease classification and prediction in this session showcases the transformative impact of data science in the medical field. Together, let us embark on this journey to advance our understanding of thyroid disease and make a difference in the lives of individuals affected by this condition. Welcome to the Data Science Crash Course on Thyroid Disease Classification and Prediction!
Book Synopsis DATA SCIENCE WORKSHOP: Lung Cancer Classification and Prediction Using Machine Learning and Deep Learning with Python GUI by : Vivian Siahaan
Download or read book DATA SCIENCE WORKSHOP: Lung Cancer Classification and Prediction Using Machine Learning and Deep Learning with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-08-12 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Data Science Workshop presents a comprehensive journey through lung cancer analysis. Beginning with data exploration, the dataset is thoroughly examined to uncover insights into its structure and contents. The focus then shifts to categorizing features and understanding their distribution patterns, revealing key trends and relationships that could impact the predictive models. To predict lung cancer using machine learning models, an extensive grid search is conducted, fine-tuning model hyperparameters for optimal performance. The iterative process involves training various models, such as K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron, and evaluating their outcomes to select the best-performing approach. Utilizing GridSearchCV aids in systematically optimizing parameters to enhance predictive accuracy. Deep Learning is harnessed through Artificial Neural Networks (ANN), which involve building multi-layered models capable of learning intricate patterns from data. The ANN architecture, comprising input, hidden, and output layers, is designed to capture the complex relationships within the dataset. Metrics like accuracy, precision, recall, and F1-score are employed to comprehensively evaluate model performance. These metrics provide a holistic view of the model's ability to classify lung cancer cases accurately and minimize false positives or negatives. The Graphical User Interface (GUI) aspect of the project is developed using PyQt, enabling user-friendly interactions with the predictive models. The GUI design includes features such as radio buttons for selecting preprocessing options (Raw, Normalization, or Standardization), a combobox for choosing the ANN model type (e.g., CNN 1D), and buttons to initiate training and prediction. The PyQt interface enhances usability by allowing users to visualize predictions, classification reports, confusion matrices, and loss-accuracy plots. The GUI's functionality expands to encompass the entire workflow. It enables data preprocessing by loading and splitting the dataset into training and testing subsets. Users can then select machine learning or deep learning models for training. The trained models are saved for future use to avoid retraining. The interface also facilitates model evaluation, showcasing accuracy scores, classification reports detailing precision and recall, and visualizations depicting loss and accuracy trends over epochs. The project's educational value lies in its comprehensive approach, taking participants through every step of a data science pipeline. Attendees gain insights into data preprocessing, model selection, hyperparameter tuning, and performance evaluation. The integration of machine learning and deep learning methodologies, along with GUI development, provides a well-rounded understanding of creating predictive tools for real-world applications. Participants leave the workshop empowered with the skills to explore and analyze medical datasets, implement machine learning and deep learning models, and build user-friendly interfaces for effective interaction. The workshop bridges the gap between theoretical knowledge and practical implementation, fostering a deeper understanding of data-driven decision-making in the realm of medical diagnostics and classification.
Book Synopsis DATA SCIENCE WORKSHOP: Cervical Cancer Classification and Prediction Using Machine Learning and Deep Learning with Python GUI by : Vivian Siahaan
Download or read book DATA SCIENCE WORKSHOP: Cervical Cancer Classification and Prediction Using Machine Learning and Deep Learning with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-08-13 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book titled " Data Science Workshop: Cervical Cancer Classification and Prediction using Machine Learning and Deep Learning with Python GUI" embarks on an insightful journey starting with an in-depth exploration of the dataset. This dataset encompasses various features that shed light on patients' medical histories and attributes. Utilizing the capabilities of pandas, the dataset is loaded, and essential details like data dimensions, column names, and data types are scrutinized. The presence of missing data is addressed by employing suitable strategies such as mean-based imputation for numerical features and categorical encoding for non-numeric ones. Subsequently, the project delves into an illuminating visualization of categorized feature distributions. Through the ingenious use of pie charts, bar plots, and heatmaps, the project unveils the distribution patterns of key attributes such as 'Hormonal Contraceptives,' 'Smokes,' 'IUD,' and others. These visualizations illuminate potential relationships between these features and the target variable 'Biopsy,' which signifies the presence or absence of cervical cancer. Such exploratory analyses serve as a vital foundation for identifying influential trends within the dataset. Transitioning into the core phase of predictive modeling, the workshop orchestrates a meticulous ensemble of machine learning models to forecast cervical cancer outcomes. The repertoire includes Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Gradient Boosting, Naïve Bayes, and the power of ensemble methods like AdaBoost and XGBoost. The models undergo rigorous hyperparameter tuning facilitated by Grid Search and Random Search to optimize predictive accuracy and precision. As the workshop progresses, the spotlight shifts to the realm of deep learning, introducing advanced neural network architectures. An Artificial Neural Network (ANN) featuring multiple hidden layers is trained using the backpropagation algorithm. Long Short-Term Memory (LSTM) networks are harnessed to capture intricate temporal relationships within the data. The arsenal extends to include Self Organizing Maps (SOMs), Restricted Boltzmann Machines (RBMs), and Autoencoders, showcasing the efficacy of unsupervised feature learning and dimensionality reduction techniques. The evaluation phase emerges as a pivotal aspect, accentuated by an array of comprehensive metrics. Performance assessment encompasses metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Cross-validation and learning curves are strategically employed to mitigate overfitting and ensure model generalization. Furthermore, visual aids such as ROC curves and confusion matrices provide a lucid depiction of the models' interplay between sensitivity and specificity. Culminating on a high note, the workshop concludes with the creation of a Python GUI utilizing PyQt. This intuitive graphical user interface empowers users to input pertinent medical data and receive instant predictions regarding their cervical cancer risk. Seamlessly integrating the most proficient classification model, this user-friendly interface bridges the gap between sophisticated data science techniques and practical healthcare applications. In this comprehensive workshop, participants navigate through the intricate landscape of data exploration, preprocessing, feature visualization, predictive modeling encompassing both traditional and deep learning paradigms, robust performance evaluation, and culminating in the development of an accessible and informative GUI. The project aspires to provide healthcare professionals and individuals with a potent tool for early cervical cancer detection and prognosis.
Book Synopsis THE APPLIED DATA SCIENCE WORKSHOP: Prostate Cancer Classification and Recognition Using Machine Learning and Deep Learning with Python GUI by : Vivian Siahaan
Download or read book THE APPLIED DATA SCIENCE WORKSHOP: Prostate Cancer Classification and Recognition Using Machine Learning and Deep Learning with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-07-19 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Applied Data Science Workshop on Prostate Cancer Classification and Recognition using Machine Learning and Deep Learning with Python GUI involved several steps and components. The project aimed to analyze prostate cancer data, explore the features, develop machine learning models, and create a graphical user interface (GUI) using PyQt5. The project began with data exploration, where the prostate cancer dataset was examined to understand its structure and content. Various statistical techniques were employed to gain insights into the data, such as checking the dimensions, identifying missing values, and examining the distribution of the target variable. The next step involved exploring the distribution of features in the dataset. Visualizations were created to analyze the characteristics and relationships between different features. Histograms, scatter plots, and correlation matrices were used to uncover patterns and identify potential variables that may contribute to the classification of prostate cancer. Machine learning models were then developed to classify prostate cancer based on the available features. Several algorithms, including Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Adaboost, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP), were implemented. Each model was trained and evaluated using appropriate techniques such as cross-validation and grid search for hyperparameter tuning. The performance of each machine learning model was assessed using evaluation metrics such as accuracy, precision, recall, and F1-score. These metrics provided insights into the effectiveness of the models in accurately classifying prostate cancer cases. Model comparison and selection were based on their performance and the specific requirements of the project. In addition to the machine learning models, a deep learning model based on an Artificial Neural Network (ANN) was implemented. The ANN architecture consisted of multiple layers, including input, hidden, and output layers. The ANN model was trained using the dataset, and its performance was evaluated using accuracy and loss metrics. To provide a user-friendly interface for the project, a GUI was designed using PyQt, a Python library for creating desktop applications. The GUI allowed users to interact with the machine learning models and perform tasks such as selecting the prediction method, loading data, training models, and displaying results. The GUI included various graphical components such as buttons, combo boxes, input fields, and plot windows. These components were designed to facilitate data loading, model training, and result visualization. Users could choose the prediction method, view accuracy scores, classification reports, and confusion matrices, and explore the predicted values compared to the actual values. The GUI also incorporated interactive features such as real-time updates of prediction results based on user selections and dynamic plot generation for visualizing model performance. Users could switch between different prediction methods, observe changes in accuracy, and examine the history of training loss and accuracy through plotted graphs. Data preprocessing techniques, such as standardization and normalization, were applied to ensure the consistency and reliability of the machine learning and deep learning models. The dataset was divided into training and testing sets to assess model performance on unseen data and detect overfitting or underfitting. Model persistence was implemented to save the trained machine learning and deep learning models to disk, allowing for easy retrieval and future use. The saved models could be loaded and utilized within the GUI for prediction tasks without the need for retraining. Overall, the Applied Data Science Workshop on Prostate Cancer Classification and Recognition provided a comprehensive framework for analyzing prostate cancer data, developing machine learning and deep learning models, and creating an interactive GUI. The project aimed to assist in the accurate classification and recognition of prostate cancer cases, facilitating informed decision-making and potentially contributing to improved patient outcomes.
Book Synopsis Cancer Prediction for Industrial IoT 4.0 by : Meenu Gupta
Download or read book Cancer Prediction for Industrial IoT 4.0 written by Meenu Gupta and published by CRC Press. This book was released on 2021-12-31 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective explores various cancers using Artificial Intelligence techniques. It presents the rapid advancement in the existing prediction models by applying Machine Learning techniques. Several applications of Machine Learning in different cancer prediction and treatment options are discussed, including specific ideas, tools and practices most applicable to product/service development and innovation opportunities. The wide variety of topics covered offers readers multiple perspectives on various disciplines. Features • Covers the fundamentals, history, reality and challenges of cancer • Presents concepts and analysis of different cancers in humans • Discusses Machine Learning-based deep learning and data mining concepts in the prediction of cancer • Offers real-world examples of cancer prediction • Reviews strategies and tools used in cancer prediction • Explores the future prospects in cancer prediction and treatment Readers will learn the fundamental concepts and analysis of cancer prediction and treatment, including how to apply emerging technologies such as Machine Learning into practice to tackle challenges in domains/fields of cancer with real-world scenarios. Hands-on chapters contributed by academicians and other professionals from reputed organizations provide and describe frameworks, applications, best practices and case studies on emerging cancer treatment and predictions. This book will be a vital resource to graduate students, data scientists, Machine Learning researchers, medical professionals and analytics managers.
Book Synopsis Advanced Machine Learning Approaches in Cancer Prognosis by : Janmenjoy Nayak
Download or read book Advanced Machine Learning Approaches in Cancer Prognosis written by Janmenjoy Nayak and published by Springer Nature. This book was released on 2021-05-29 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learning have proved its vast range of significance and, provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D. students, postdocs, and anyone interested in the subjects discussed.
Book Synopsis Deep Learning for Data Analytics by : Himansu Das
Download or read book Deep Learning for Data Analytics written by Himansu Das and published by Academic Press. This book was released on 2020-05-29 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis. Presents the latest advances in Deep Learning for data analytics and biomedical engineering applications. Discusses Deep Learning techniques as they are being applied in the real world of biomedical engineering and data science, including Deep Learning networks, deep feature learning, deep learning toolboxes, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networks Provides readers with an introduction to Deep Learning, along with coverage of deep belief networks, convolutional neural networks, Restricted Boltzmann Machines, data analytics basics, enterprise data science, predictive analysis, optimization for Deep Learning, and feature selection using Deep Learning
Book Synopsis THE APPLIED DATA SCIENCE WORKSHOP: Urinary biomarkers Based Pancreatic Cancer Classification and Prediction Using Machine Learning with Python GUI by : Vivian Siahaan
Download or read book THE APPLIED DATA SCIENCE WORKSHOP: Urinary biomarkers Based Pancreatic Cancer Classification and Prediction Using Machine Learning with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-07-23 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Applied Data Science Workshop on "Urinary Biomarkers-Based Pancreatic Cancer Classification and Prediction Using Machine Learning with Python GUI" embarks on a comprehensive journey, commencing with an in-depth exploration of the dataset. During this initial phase, the structure and size of the dataset are thoroughly examined, and the various features it contains are meticulously studied. The principal objective is to understand the relationship between these features and the target variable, which, in this case, is the diagnosis of pancreatic cancer. The distribution of each feature is analyzed, and potential patterns, trends, or outliers that could significantly impact the model's performance are identified. To ensure the data is in optimal condition for model training, preprocessing steps are undertaken. This involves handling missing values through imputation techniques, such as mean, median, or interpolation, depending on the nature of the data. Additionally, feature engineering is performed to derive new features or transform existing ones, with the aim of enhancing the model's predictive power. In preparation for model building, the dataset is split into training and testing sets. This division is crucial to assess the models' generalization performance on unseen data accurately. To maintain a balanced representation of classes in both sets, stratified sampling is employed, mitigating potential biases in the model evaluation process. The workshop explores an array of machine learning classifiers suitable for pancreatic cancer classification, such as Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Adaboost, Extreme Gradient Boosting, Light Gradient Boosting, Naïve Bayes, and Multi-Layer Perceptron (MLP). For each classifier, three different preprocessing techniques are applied to investigate their impact on model performance: raw (unprocessed data), normalization (scaling data to a similar range), and standardization (scaling data to have zero mean and unit variance). To optimize the classifiers' hyperparameters and boost their predictive capabilities, GridSearchCV, a technique for hyperparameter tuning, is employed. GridSearchCV conducts an exhaustive search over a specified hyperparameter grid, evaluating different combinations to identify the optimal settings for each model and preprocessing technique. During the model evaluation phase, multiple performance metrics are utilized to gauge the efficacy of the classifiers. Commonly used metrics include accuracy, recall, precision, and F1-score. By comprehensively assessing these metrics, the strengths and weaknesses of each model are revealed, enabling a deeper understanding of their performance across different classes of pancreatic cancer. Classification reports are generated to present a detailed breakdown of the models' performance, including precision, recall, F1-score, and support for each class. These reports serve as valuable tools for interpreting model outputs and identifying areas for potential improvement. The workshop highlights the significance of graphical user interfaces (GUIs) in facilitating user interactions with machine learning models. By integrating PyQt, a powerful GUI development library for Python, participants create a user-friendly interface that enables users to interact with the models effortlessly. The GUI provides options to select different preprocessing techniques, visualize model outputs such as confusion matrices and decision boundaries, and gain insights into the models' classification capabilities. One of the primary advantages of the graphical user interface is its ability to offer users a seamless and intuitive experience in predicting and classifying pancreatic cancer based on urinary biomarkers. The GUI empowers users to make informed decisions by allowing them to compare the performance of different classifiers under various preprocessing techniques. Throughout the workshop, a strong emphasis is placed on the significance of proper data preprocessing, hyperparameter tuning, and robust model evaluation. These crucial steps contribute to building accurate and reliable machine learning models for pancreatic cancer prediction. By the culmination of the workshop, participants have gained valuable hands-on experience in data exploration, machine learning model building, hyperparameter tuning, and GUI development, all geared towards addressing the specific challenge of pancreatic cancer classification and prediction. In conclusion, the Applied Data Science Workshop on "Urinary Biomarkers-Based Pancreatic Cancer Classification and Prediction Using Machine Learning with Python GUI" embarks on a comprehensive and transformative journey, bringing together data exploration, preprocessing, machine learning model selection, hyperparameter tuning, model evaluation, and GUI development. The project's focus on pancreatic cancer prediction using urinary biomarkers aligns with the pressing need for early detection and treatment of this deadly disease. As participants delve into the intricacies of machine learning and medical research, they contribute to the broader scientific community's ongoing efforts to combat cancer and improve patient outcomes. Through the integration of data science methodologies and powerful visualization tools, the workshop exemplifies the potential of machine learning in revolutionizing medical diagnostics and healthcare practices.
Book Synopsis The Applied Data Science Workshop On Medical Datasets Using Machine Learning and Deep Learning with Python GUI by : Vivian Siahaan
Download or read book The Applied Data Science Workshop On Medical Datasets Using Machine Learning and Deep Learning with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on with total page 1574 pages. Available in PDF, EPUB and Kindle. Book excerpt: Workshop 1: Heart Failure Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI Cardiovascular diseases (CVDs) are the number 1 cause of death globally taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure. People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning models can be of great help. Dataset used in this project is from Davide Chicco, Giuseppe Jurman. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making 20, 16 (2020). Attribute information in the dataset are as follows: age: Age; anaemia: Decrease of red blood cells or hemoglobin (boolean); creatinine_phosphokinase: Level of the CPK enzyme in the blood (mcg/L); diabetes: If the patient has diabetes (boolean); ejection_fraction: Percentage of blood leaving the heart at each contraction (percentage); high_blood_pressure: If the patient has hypertension (boolean); platelets: Platelets in the blood (kiloplatelets/mL); serum_creatinine: Level of serum creatinine in the blood (mg/dL); serum_sodium: Level of serum sodium in the blood (mEq/L); sex: Woman or man (binary); smoking: If the patient smokes or not (boolean); time: Follow-up period (days); and DEATH_EVENT: If the patient deceased during the follow-up period (boolean). The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performace of the model, scalability of the model, training loss, and training accuracy. WORKSHOP 2: Cervical Cancer Classification and Prediction Using Machine Learning and Deep Learning with Python GUI About 11,000 new cases of invasive cervical cancer are diagnosed each year in the U.S. However, the number of new cervical cancer cases has been declining steadily over the past decades. Although it is the most preventable type of cancer, each year cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide. Numerous studies report that high poverty levels are linked with low screening rates. In addition, lack of health insurance, limited transportation, and language difficulties hinder a poor woman’s access to screening services. Human papilloma virus (HPV) is the main risk factor for cervical cancer. In adults, the most important risk factor for HPV is sexual activity with an infected person. Women most at risk for cervical cancer are those with a history of multiple sexual partners, sexual intercourse at age 17 years or younger, or both. A woman who has never been sexually active has a very low risk for developing cervical cancer. Sexual activity with multiple partners increases the likelihood of many other sexually transmitted infections (chlamydia, gonorrhea, syphilis). Studies have found an association between chlamydia and cervical cancer risk, including the possibility that chlamydia may prolong HPV infection. Therefore, early detection of cervical cancer using machine and deep learning models can be of great help. The dataset used in this project is obtained from UCI Repository and kindly acknowledged. This file contains a List of Risk Factors for Cervical Cancer leading to a Biopsy Examination. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performace of the model, scalability of the model, training loss, and training accuracy. WORKSHOP 3: Chronic Kidney Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI Chronic kidney disease is the longstanding disease of the kidneys leading to renal failure. The kidneys filter waste and excess fluid from the blood. As kidneys fail, waste builds up. Symptoms develop slowly and aren't specific to the disease. Some people have no symptoms at all and are diagnosed by a lab test. Medication helps manage symptoms. In later stages, filtering the blood with a machine (dialysis) or a transplant may be required The dataset used in this project was taken over a 2-month period in India with 25 features (eg, red blood cell count, white blood cell count, etc). The target is the 'classification', which is either 'ckd' or 'notckd' - ckd=chronic kidney disease. It contains measures of 24 features for 400 people. Quite a lot of features for just 400 samples. There are 14 categorical features, while 10 are numerical. The dataset needs cleaning: in that it has NaNs and the numeric features need to be forced to floats. Attribute Information: Age(numerical) age in years; Blood Pressure(numerical) bp in mm/Hg; Specific Gravity(categorical) sg - (1.005,1.010,1.015,1.020,1.025); Albumin(categorical) al - (0,1,2,3,4,5); Sugar(categorical) su - (0,1,2,3,4,5); Red Blood Cells(categorical) rbc - (normal,abnormal); Pus Cell (categorical) pc - (normal,abnormal); Pus Cell clumps(categorical) pcc - (present, notpresent); Bacteria(categorical) ba - (present,notpresent); Blood Glucose Random(numerical) bgr in mgs/dl; Blood Urea(numerical) bu in mgs/dl; Serum Creatinine(numerical) sc in mgs/dl; Sodium(numerical) sod in mEq/L; Potassium(numerical) pot in mEq/L; Hemoglobin(numerical) hemo in gms; Packed Cell Volume(numerical); White Blood Cell Count(numerical) wc in cells/cumm; Red Blood Cell Count(numerical) rc in millions/cmm; Hypertension(categorical) htn - (yes,no); Diabetes Mellitus(categorical) dm - (yes,no); Coronary Artery Disease(categorical) cad - (yes,no); Appetite(categorical) appet - (good,poor); Pedal Edema(categorical) pe - (yes,no); Anemia(categorical) ane - (yes,no); and Class (categorical) class - (ckd,notckd). The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performace of the model, scalability of the model, training loss, and training accuracy. WORKSHOP 4: Lung Cancer Classification and Prediction Using Machine Learning and Deep Learning with Python GUI The effectiveness of cancer prediction system helps the people to know their cancer risk with low cost and it also helps the people to take the appropriate decision based on their cancer risk status. The data is collected from the website online lung cancer prediction system. Total number of attributes in the dataset is 16, while number of instances is 309. Following are attribute information of dataset: Gender: M(male), F(female); Age: Age of the patient; Smoking: YES=2 , NO=1; Yellow fingers: YES=2 , NO=1; Anxiety: YES=2 , NO=1; Peer_pressure: YES=2 , NO=1; Chronic Disease: YES=2 , NO=1; Fatigue: YES=2 , NO=1; Allergy: YES=2 , NO=1; Wheezing: YES=2 , NO=1; Alcohol: YES=2 , NO=1; Coughing: YES=2 , NO=1; Shortness of Breath: YES=2 , NO=1; Swallowing Difficulty: YES=2 , NO=1; Chest pain: YES=2 , NO=1; and Lung Cancer: YES , NO. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performace of the model, scalability of the model, training loss, and training accuracy. WORKSHOP 5: Alzheimer’s Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI Alzheimer's is a type of dementia that causes problems with memory, thinking and behavior. Symptoms usually develop slowly and get worse over time, becoming severe enough to interfere with daily tasks. Alzheimer's is not a normal part of aging. The greatest known risk factor is increasing age, and the majority of people with Alzheimer's are 65 and older. But Alzheimer's is not just a disease of old age. Approximately 200,000 Americans under the age of 65 have younger-onset Alzheimer’s disease (also known as early-onset Alzheimer’s). The dataset consists of a longitudinal MRI data of 374 subjects aged 60 to 96. Each subject was scanned at least once. Everyone is right-handed. 206 of the subjects were grouped as 'Nondemented' throughout the study. 107 of the subjects were grouped as 'Demented' at the time of their initial visits and remained so throughout the study. 14 subjects were grouped as 'Nondemented' at the time of their initial visit and were subsequently characterized as 'Demented' at a later visit. These fall under the 'Converted' category. Following are some important features in the dataset: EDUC:Years of Education; SES: Socioeconomic Status; MMSE: Mini Mental State Examination; CDR: Clinical Dementia Rating; eTIV: Estimated Total Intracranial Volume; nWBV: Normalize Whole Brain Volume; and ASF: Atlas Scaling Factor. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. WORKSHOP 6: Parkinson Classification and Prediction Using Machine Learning and Deep Learning with Python GUI The dataset was created by Max Little of the University of Oxford, in collaboration with the National Centre for Voice and Speech, Denver, Colorado, who recorded the speech signals. The original study published the feature extraction methods for general voice disorders. This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD). Each column in the table is a particular voice measure, and each row corresponds one of 195 voice recording from these individuals ("name" column). The main aim of the data is to discriminate healthy people from those with PD, according to "status" column which is set to 0 for healthy and 1 for PD. The data is in ASCII CSV format. The rows of the CSV file contain an instance corresponding to one voice recording. There are around six recordings per patient, the name of the patient is identified in the first column. Attribute information of this dataset are as follows: name - ASCII subject name and recording number; MDVP:Fo(Hz) - Average vocal fundamental frequency; MDVP:Fhi(Hz) - Maximum vocal fundamental frequency; MDVP:Flo(Hz) - Minimum vocal fundamental frequency; MDVP:Jitter(%); MDVP:Jitter(Abs); MDVP:RAP; MDVP:PPQ; Jitter:DDP – Several measures of variation in fundamental frequency; MDVP:Shimmer; MDVP:Shimmer(dB); Shimmer:APQ3; Shimmer:APQ5; MDVP:APQ; Shimmer:DDA - Several measures of variation in amplitude; NHR; HNR - Two measures of ratio of noise to tonal components in the voice; status - Health status of the subject (one) - Parkinson's, (zero) – healthy; RPDE,D2 - Two nonlinear dynamical complexity measures; DFA - Signal fractal scaling exponent; and spread1,spread2,PPE - Three nonlinear measures of fundamental frequency variation. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. WORKSHOP 7: Liver Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI Patients with Liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. This dataset was used to evaluate prediction algorithms in an effort to reduce burden on doctors. This dataset contains 416 liver patient records and 167 non liver patient records collected from North East of Andhra Pradesh, India. The "Dataset" column is a class label used to divide groups into liver patient (liver disease) or not (no disease). This data set contains 441 male patient records and 142 female patient records. Any patient whose age exceeded 89 is listed as being of age "90". Columns in the dataset: Age of the patient; Gender of the patient; Total Bilirubin; Direct Bilirubin; Alkaline Phosphotase; Alamine Aminotransferase; Aspartate Aminotransferase; Total Protiens; Albumin; Albumin and Globulin Ratio; and Dataset: field used to split the data into two sets (patient with liver disease, or no disease). The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.
Book Synopsis Deep Learning in Cancer Diagnostics by : Mohd Hafiz Arzmi
Download or read book Deep Learning in Cancer Diagnostics written by Mohd Hafiz Arzmi and published by Springer Nature. This book was released on 2023-01-18 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cancer is the leading cause of mortality in most, if not all, countries around the globe. It is worth noting that the World Health Organisation (WHO) in 2019 estimated that cancer is the primary or secondary leading cause of death in 112 of 183 countries for individuals less than 70 years old, which is alarming. In addition, cancer affects socioeconomic development as well. The diagnostics of cancer are often carried out by medical experts through medical imaging; nevertheless, it is not without misdiagnosis owing to a myriad of reasons. With the advancement of technology and computing power, the use of state-of-the-art computational methods for the accurate diagnosis of cancer is no longer far-fetched. In this brief, the diagnosis of four types of common cancers, i.e., breast, lung, oral and skin, are evaluated with different state-of-the-art feature-based transfer learning models. It is expected that the findings in this book are insightful to various stakeholders in the diagnosis of cancer.
Book Synopsis Deep Learning for Cancer Diagnosis by : Utku Kose
Download or read book Deep Learning for Cancer Diagnosis written by Utku Kose and published by Springer Nature. This book was released on 2020-09-12 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores various applications of deep learning to the diagnosis of cancer,while also outlining the future face of deep learning-assisted cancer diagnostics. As is commonly known, artificial intelligence has paved the way for countless new solutions in the field of medicine. In this context, deep learning is a recent and remarkable sub-field, which can effectively cope with huge amounts of data and deliver more accurate results. As a vital research area, medical diagnosis is among those in which deep learning-oriented solutions are often employed. Accordingly, the objective of this book is to highlight recent advanced applications of deep learning for diagnosing different types of cancer. The target audience includes scientists, experts, MSc and PhD students, postdocs, and anyone interested in the subjects discussed. The book can be used as a reference work to support courses on artificial intelligence, medical and biomedicaleducation.
Book Synopsis Computational Analysis and Deep Learning for Medical Care by : Amit Kumar Tyagi
Download or read book Computational Analysis and Deep Learning for Medical Care written by Amit Kumar Tyagi and published by John Wiley & Sons. This book was released on 2021-08-10 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book details deep learning models like ANN, RNN, LSTM, in many industrial sectors such as transportation, healthcare, military, agriculture, with valid and effective results, which will help researchers find solutions to their deep learning research problems. We have entered the era of smart world devices, where robots or machines are being used in most applications to solve real-world problems. These smart machines/devices reduce the burden on doctors, which in turn make their lives easier and the lives of their patients better, thereby increasing patient longevity, which is the ultimate goal of computer vision. Therefore, the goal in writing this book is to attempt to provide complete information on reliable deep learning models required for e-healthcare applications. Ways in which deep learning can enhance healthcare images or text data for making useful decisions are discussed. Also presented are reliable deep learning models, such as neural networks, convolutional neural networks, backpropagation, and recurrent neural networks, which are increasingly being used in medical image processing, including for colorization of black and white X-ray images, automatic machine translation images, object classification in photographs/images (CT scans), character or useful generation (ECG), image caption generation, etc. Hence, reliable deep learning methods for the perception or production of better results are a necessity for highly effective e-healthcare applications. Currently, the most difficult data-related problem that needs to be solved concerns the rapid increase of data occurring each day via billions of smart devices. To address the growing amount of data in healthcare applications, challenges such as not having standard tools, efficient algorithms, and a sufficient number of skilled data scientists need to be overcome. Hence, there is growing interest in investigating deep learning models and their use in e-healthcare applications. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in transportation, healthcare, biomedicine, military, agriculture.
Book Synopsis Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning by : Rani, Geeta
Download or read book Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning written by Rani, Geeta and published by IGI Global. This book was released on 2020-10-16 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt: By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.
Book Synopsis Artificial Intelligence and Deep Learning in Pathology by : Stanley Cohen
Download or read book Artificial Intelligence and Deep Learning in Pathology written by Stanley Cohen and published by Elsevier Health Sciences. This book was released on 2020-06-02 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern recognition and image interpretation for diagnosis and prognosis. Yet most pathologists have just a passing knowledge of data mining, machine learning, and AI, and little exposure to the vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial Intelligence and Deep Learning in Pathology, Dr. Stanley Cohen covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to pathologists at all levels of experience. Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible. Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning. Discusses and explains recent accomplishments such as algorithms used to diagnose skin cancer from photographs, AI-based platforms developed to identify lesions of the retina, using computer vision to interpret electrocardiograms, identifying mitoses in cancer using learning algorithms vs. signal processing algorithms, and many more.
Book Synopsis Skin Cancer Detection Using Generative Adversarial Network and an Ensemble of Deep Convolutional Neural Networks by : Aakriti Adhikari
Download or read book Skin Cancer Detection Using Generative Adversarial Network and an Ensemble of Deep Convolutional Neural Networks written by Aakriti Adhikari and published by . This book was released on 2019 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past few years, Deep learning has been widely used in medical imaging for classification and segmentation and has been successful in providing better diagnostic accuracy. The state-of- the-art deep learning algorithms are built using neural networks arranged in layers where the first layer extracts basic information of images like edges, colors etc so that the output of one layer is fed as input to the next consecutive layers. Thus, increasing the complexity of learning with increase of layers. In comparison with the traditional machine learning algorithms, deep learning has many advantages and is an automatic process. However, it requires large scale annotated data for better performance and is thus constrained by limited size of available public datasets. To overcome data constraints, this thesis proposes a more efficient and novel scheme comprising techniques that use an ensemble of convolution neural networks (CNN) with generative adversarial network (GAN) based augmentation to improve the diagnostic accuracy. Also, the implementation of the proposed technique on skin lesion dataset of limited size to classify melanoma is presented. In this work, images are augmented to increase the dataset size using two different augmentation techniques: traditional augmentation and GAN based augmentation. GAN based augmentation is based on neural networks, where, two neural networks compete against each other to produce visually realistic synthetic images. For the classification, CNN and an ensemble of CNNs are trained on the final enlarged training dataset comprising original images and synthetic images. Here, the ensemble of CNN techniques combines five trained CNNs into a single meta-classifier. One hundred and ninety-three skin lesion test images were used to validate our proposed methods. The first method proposed in this research incorporated traditional to enlarge the training dataset and trained a CNN classifier to classify skin cancer into malignant or benign. The performance of the model on the test dataset was observed with 79.29% of accuracy, 77% sensitivity and 81.38% specificity. The second proposed method incorporated both traditional augmentation and GAN based augmentation to enlarge the training dataset and a CNN classifier was trained in a similar way as compared to the first method. This method was validated with 81.32% accuracy, 80.26% sensitivity and 82.78% specificity. The final proposed method utilized both of these augmentation techniques to enlarge the training dataset and an ensemble of five CNN classifiers was trained for the classification. It performed better with the performance accuracy of 84.30% , sensitivity of 84.32% and specificity of 84.21% . Further, it prevented trapping into local maxima, causing performance to increase. The scheme with the proposed methods can be a better choice while dealing with the limited training dataset.
