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.
Author(s): Teik Toe Teoh
Series: SpringerBriefs in Computer Science
Publisher: Springer
Year: 2023
Language: English
Pages: 102
City: Singapore
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
6.2.1 Non-proliferative Diabetic Retinopathy
6.2.2 Proliferative Diabetic Retinopathy
6.3 Classifying Diabetic Retinopathy
6.3.1 Diabetic Retinopathy
6.3.2 Deep Learning and Image Identification
6.3.3 Training
Data Preparation
Setting of the Experiment
6.3.4 Result and Evaluation
Accuracy
Loss
6.3.5 The Optimisation of Convolutional Neural Network
Improvement of the Model
Results After Improvement
6.3.6 Comparison with Other Models
6.3.7 Conclusion
References
Index
