Title Convolutional neural networks for medical applications / Teik Toe Teoh

Published Singapore : Springer, [2023]

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Description 1 online resource (xi, 96 pages) : illustrations (black and white) Series SpringerBriefs in computer science, 2191-5776 SpringerBriefs in computer science. 2191-5776 Contents Intro -- Preface -- Contents -- 1 Introduction -- 1.1 Medical Imaging -- 1.1.1 Example of An X-ray Image -- 1.1.2 Users of Medical Imaging -- 1.1.3 Importance of Medical Imaging -- 1.2 Convolutional Neural Networks -- 1.2.1 The Convolution Operation -- 1.2.2 Pooling -- 1.2.3 Flattening -- 1.2.4 CNN Architectures -- VGG16 -- InceptionNet -- ResNet -- 1.2.5 Finetuning -- 1.3 Data Augmentation -- 1.4 Regularization -- 1.4.1 Ridge Regression -- 1.4.2 Lasso Regression -- 1.4.3 Dropout -- References -- 2 CNN for Brain Tumor Classification -- 2.1 Introduction to Brain Tumors -- 2.1.1 Benign Tumors 2.1.2 Malignant Tumors -- 2.2 Brain Tumor Dataset -- 2.2.1 Glioma Tumor -- Who Does it Affect -- Survival Rates -- Complications -- 2.2.2 Meningioma Tumor -- Who Does it Affect -- Survival Rates -- Complications -- 2.2.3 Pituitary Tumor -- Who Does it Affect -- Survival Rates -- Symptoms -- Complications -- 2.3 Classifying Brain Tumors -- 2.3.1 Data Augmentation Method -- RandomColor -- Flip or Rotation -- Mixup -- 2.3.2 Convolution and Pooling Layers -- 2.3.3 Global Average Pooling (GAP) -- 2.3.4 Training -- 2.3.5 Result -- 2.3.6 Conclusion -- References 3 CNN for Pneumonia Image Classification -- 3.1 Introduction to Pneumonia -- 3.1.1 Causes of Pneumonia -- 3.1.2 Categories of Pneumonia -- 3.1.3 Risk Factors for Pneumonia -- 3.1.4 Complications of Pneumonia -- 3.2 Pneumonia Dataset -- 3.3 Classifying Pneumonia -- 3.3.1 Methodology -- Data Pre-processing -- Convolution and Pooling Layers -- 3.3.2 Model Compilation -- Optimizer -- Loss -- Compilation -- 3.3.3 Model Evaluation -- Accuracy -- LogLoss -- Precision and Recall -- F1 Score -- 3.3.4 Model Improvement -- 3.3.5 Conclusion -- References -- 4 CNN for White Blood Cell Classification 4.1 Introduction to White Blood Cells -- 4.2 White Blood Cells Dataset -- 4.2.1 Eosinophil -- Low Eosinophil Count -- High Eosinophil Count -- 4.2.2 Lymphocyte -- Low Lymphocyte Count -- High Lymphocyte Count -- 4.2.3 Monocyte -- Low Monocyte Count -- High Monocyte Count -- 4.2.4 Neutrophil -- Low Neutrophil Count -- High Neutrophil Count -- 4.3 Classifying White Blood Cells -- 4.3.1 EfficientNet -- Model Structure (EfficientNet Model) -- Advantage of EfficientNet Model -- 4.3.2 Experimental Study -- Preprocessing Data -- Training Model -- Results and Evaluation -- 4.3.3 Conclusion -- References 5 CNN for Skin Cancer Classification -- 5.1 Introduction to Skin Cancer -- 5.1.1 Basal Cell Carcinoma -- 5.1.2 Squamous Cell Carcinoma -- 5.1.3 Melanoma -- 5.2 Skin Cancer Dataset -- 5.3 Classifying Skin Cancer -- 5.3.1 Data Pre-processing -- 5.3.2 Convolution and Pooling Layer -- 5.3.3 Final Pooling Operation -- Flatten -- Global Average Pooling -- Line Average Pooling -- Loss Function and Optimizer -- 5.3.4 Training -- Training Procedure -- Result -- Improvement of the Model -- 5.3.5 Conclusion -- References -- 6 CNN for Diabetic Retinopathy Detection -- 6.1 Introduction to Diabetic Retinopathy -- 6.2 Diabetic Retinopathy Dataset Summary Convolutional Neural Networks for Medical Applications consists of research investigated by the author, containing state-of-the-art knowledge, authored by Dr Teoh Teik Toe, in applying Convolutional Neural Networks (CNNs) to the medical imagery domain. This book will expose researchers to various applications and techniques applied with deep learning on medical images, as well as unique techniques to enhance the performance of these networks.Through the various chapters and topics covered, this book provides knowledge about the fundamentals of deep learning to a common reader while allowing a research scholar to identify some futuristic problem areas. The topics covered include brain tumor classification, pneumonia image classification, white blood cell classification, skin cancer classification and diabetic retinopathy detection. The first chapter will begin by introducing various topics used in training CNNs to help readers with common concepts covered across the book. Each chapter begins by providing information about the disease, its implications to the affected and how the use of CNNs can help to tackle issues faced in healthcare. Readers would be exposed to various performance enhancement techniques, which have been tried and tested successfully, such as specific data augmentations and image processing techniques utilized to improve the accuracy of the models Notes Includes index Description based on online resource; title from digital title page (viewed on May 04, 2023) Subject Medicine -- Data processing. Diagnostic imaging. Neural networks (Neurobiology) Computer vision. Artificial intelligence. Artificial intelligence -- Data processing. Diagnostic Imaging Artificial Intelligence artificial intelligence. Artificial intelligence Artificial intelligence -- Data processing Computer vision Diagnostic imaging Medicine -- Data processing Neural networks (Neurobiology) Form Electronic book ISBN 9789811988141 9811988145

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