Machine Learning and Deep Learning Techniques for Medical Science

✍ Scribed by K. Gayathri Devi, Kishore Balasubramanian, Le Anh Ngoc

• Publisher: CRC Press
• Year: 2022
• Tongue: English
• Leaves: 413
• Series: Artificial Intelligence (AI): Elementary to Advanced Practices
• Category: Library

✦ Synopsis

The application of machine learning is growing exponentially into every branch of business and science, including medical science. This book presents the integration of machine learning (ML) and deep learning (DL) algorithms that can be applied in the healthcare sector to reduce the time required by doctors, radiologists, and other medical professionals for analyzing, predicting, and diagnosing the conditions with accurate results. The book offers important key aspects in the development and implementation of ML and DL approaches toward developing prediction tools and models and improving medical diagnosis.

The contributors explore the recent trends, innovations, challenges, and solutions, as well as case studies of the applications of ML and DL in intelligent system-based disease diagnosis. The chapters also highlight the basics and the need for applying mathematical aspects with reference to the development of new medical models. Authors also explore ML and DL in relation to artificial intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, and pattern recognition approaches to functional magnetic resonance imaging images.

This book is for students and researchers of computer science and engineering, electronics and communication engineering, and information technology; for biomedical engineering researchers, academicians, and educators; and for students and professionals in other areas of the healthcare sector.

• Presents key aspects in the development and the implementation of ML and DL approaches toward developing prediction tools, models, and improving medical diagnosis

• Discusses the recent trends, innovations, challenges, solutions, and applications of intelligent system-based disease diagnosis

• Examines DL theories, models, and tools to enhance health information systems

• Explores ML and DL in relation to AI prediction tools, discovery of drugs, neuroscience, and diagnosis in multiple imaging modalities

Dr. K. Gayathri Devi is a Professor at the Department of Electronics and Communication Engineering, Dr. N.G.P Institute of Technology, Tamil Nadu, India.

Dr. Kishore Balasubramanian is an Assistant Professor (Senior Scale) at the Department of EEE at Dr. Mahalingam College of Engineering & Technology, Tamil Nadu, India.

Dr. Le Anh Ngoc is a Director of Swinburne Innovation Space and Professor in Swinburne University of Technology (Vietnam).

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
Editor Biographies
Contributors
1. A Comprehensive Study on MLP and CNN, and the Implementation of Multi-Class Image Classification using Deep CNN
1.1 Introduction
1.2 The Processes of the Neural Network
1.2.1 Basics of Neural Network
1.2.1.1 Architecture of Neural Network
1.2.1.2 Working Principles of Neural Network
1.2.1.3 Learning Methods of Neural Network
1.2.1.4 Drawbacks of Neural Network
1.2.2 Convolutional Neural Network (CNN) Algorithm
1.2.2.1 Merits of CNN over MLP
1.2.2.2 Contents of CNN
1.2.2.3 Working of CNN Algorithm
1.2.2.3.1 Convolution Layer
Padding
Striding
1.2.2.3.2 Pooling Layer
Pooling Layer Types
1.2.2.3.3 Fully Connected Layer (FC)
1.2.2.3.4 Dropout
1.2.2.3.5 Activation Functions
1.2.2.4 Deep CNN
1.3 Experimental Procedure
1.3.1 Preparing the Dataset
1.3.2 Model Training and Testing
1.4 Results and Discussion
1.4.1 MNIST Dataset Image Classifications
1.4.2 CIFAR-10 Dataset Image Classifications
1.5 Conclusion
References
2. An Efficient Technique for Image Compression and Quality Retrieval in Diagnosis of Brain Tumour Hyper Spectral Image
2.1 Introduction
2.2 Literature Survey
2.2.1 Proposed System
2.2.2 Data Latches with D-flip Flops
2.2.3 Discussion and Results of the Simulation
2.3 Conclusion
References
3. Classification of Breast Thermograms using a Multi-layer Perceptron with Back Propagation Learning
3.1 Introduction
3.2 Related Works
3.3 Methods & Materials
3.3.1 Pre-processing of Thermograms & Region of Interest (ROI) Segmentation
3.3.2 Feature Extraction & Selection
3.3.3 Designing Steps of a Multi-Layer Perceptron with Back Propagation Learning
3.3.3.1 Phase I: Feedforward Computations
3.3.3.2 Phase II: Back Propagation of the Error
3.3.3.3 Phase III: Update Weights and Error in the Output and Hidden Units
3.4 Performance Evaluation Parameters
3.5 Classification Results & Discussion
3.5.1 ANN Model with 5 Neurons in Hidden Layer
3.5.2 ANN Model with 10 Neurons in Hidden Layer
3.5.3 ANN Model with 15 Neurons in Hidden Layer
3.6 Conclusion & Future Work
References
4. Neural Networks for Medical Image Computing
4.1 Introduction
4.2 Structure of Neural Network
4.3 Learning Process in Neural Networks
4.3.1 Supervised Learning
4.3.1.1 An Overview of Supervised Learning
4.3.1.2 Supervised Learning in Medical Image Processing
4.3.2 Unsupervised Learning
4.3.2.1 Unsupervised Learning
4.3.2.2 Overview of Competitive Learning
4.3.2.3 Medical Analysis using Unsupervised Learning
4.3.3 Reinforcement Learning
4.3.3.1 Reinforcement Learning
4.3.3.2 Overview of Q-Learning
4.3.3.3 Adopting Reinforcement Learning in Health Sector
4.4 Types of Neural Networks
4.4.1 Perceptron
4.4.1.1 Perceptron
4.4.1.2 Perceptron in Medical Image Analysis
4.4.2 Radial Basis Function Network
4.4.2.1 Architecture of Radial Basis Function Network
4.4.2.2 Implementing Radial Basis Function in Medical Analysis
4.4.3 Convolutional Neural Network
4.4.3.1 Architecture of Convolutional Neural Network
4.4.3.2 Convolutional Neural Networks in Medical Diagnosis
4.4.4 Recurrent Neural Network
4.4.4.1 Introduction to Recurrent Neural Network
4.4.4.2 Types of Recurrent Neural Network
4.4.4.3 Medical Analysis using Recurrent Neural Network
4.4.5 Hopfield Neural Network
4.4.5.1 Overview of Hopfield Neural Network
4.4.5.2 Hopfield Neural Network in Medical Diagnosis
4.5 Conclusion
References
5. Recent Trends in Bio-Medical Waste, Challenges and Opportunities
5.1 Introduction
5.2 Waste Disposal
5.3 Health Care Industries
5.4 Conclusion
References
6. Teager-Kaiser Boost Clustered Segmentation of Retinal Fundus Images for Glaucoma Detection
6.1 Preamble
6.2 Methodology
6.2.1 Nonlinear Teager-Kaiser Filtering Technique
6.2.2 Teager-Kaiser Boost Clustered Segmentation
6.2.3 Clinical Feature Extraction
6.3 Results and Discussion
6.3.1 Quantitative Analysis
6.4 Conclusion
References
7. IoT-Based Deep Neural Network Approach for Heart Rate and SpO2 Prediction
7.1 Introduction
7.1.1 Related Work
7.1.2 Motivations
7.2 Materials and Methods
7.2.1 Complete DNN-based System
7.2.2 Principal Component Analysis (PCA)
7.2.3 DNN Model
7.2.4 Cloud Computing
7.3 Results
7.3.1 DNN Model Accuracy Performance
7.3.2 System Validation
7.3.2.1 Bland-Altman Analysis
7.3.2.2 R2 (Coefficient of Determination) Regression Score Function
7.3.3 Performance Analysis Criteria
7.4 Discussion
7.5 Conclusion
References
8. An Intelligent System for Diagnosis and Prediction of Breast Cancer Malignant Features using Machine Learning Algorithms
8.1 Introduction
8.2 Machine Learning Technologies
8.2.1 Naïve Bayes
8.2.2 K-Nearest Neighbor
8.2.3 Random Forest
8.2.4 Support Vector Machine
8.3 Related Work
8.4 Proposed Methodology
8.4.1 Experimental Results and Discussions
8.4.1.1 Efficiency
8.5 Conclusion
References
9. Medical Image Classification with Artificial and Deep Convolutional Neural Networks: A Comparative Study
9.1 Introduction
9.2 Machine and Deep Learning Methods
9.2.1 Machine Learning Techniques
9.2.1.1 Supervised Learning
9.2.1.2 Unsupervised Learning
9.2.1.3 Semi-Supervised Learning
9.2.1.4 Reinforcement Learning
9.2.1.5 Deep Learning
9.2.2 Deep Learning Techniques
9.2.2.1 DL Definitions
9.2.2.2 DL Class
9.2.2.3 Deep Architectures
9.3 Comprehensive Study
9.3.1 Concept of Brain MRI Data
9.3.2 Image Classification for Medical Disease Diagnosis
9.3.3 Medical Image Classification for Machine and Deep Learning
9.4 Comparative Study
9.5 Artificial and Convolutional Deep Neural Networks based on Medical Image Classification for Alzheimer Disease
9.5.1 Brain MRI Datasets
9.5.2 MRI Data Pre-processing
9.5.3 Features Extraction and Selection from Brain MRI Datasets
9.5.4 Classification Methods
9.5.5 Proposed Machine-Deep Model
9.6 Discussion and Conclusion
References
10. Convolutional Neural Network for Classification of Skin Cancer Images
10.1 Introduction
10.2 State-of-the-Art
10.3 Materials and Methods
10.3.1 Data Preprocessing and Augmentation
10.3.2 Data Augmentation
10.3.3 Classification Models
10.3.3.1 Convolutional Neural Network (CNN)
10.3.3.2 Transfer Learning and Pre-trained Models
10.3.3.3 Pre-trained Xception Model
10.3.3.4 Xception Model Fine-tuning
10.3.3.5 Evaluation Metrics
10.4 Experimental Results
10.4.1 Learning Performance
10.4.2 Classification Results
10.4.3 Comparative Study
10.5 Conclusion and Perspectives
References
11. Application of Artificial Intelligence in Medical Imaging
11.1 Introduction
11.2 Machine Learning
11.2.1 Supervised Learning
11.2.2 Unsupervised Learning
11.2.3 Semi-supervised Learning
11.2.4 Active Learning
11.2.5 Reinforcement Learning
11.2.6 Evolutionary Learning
11.2.7 Deep Learning
11.3 Use of Machine Learning for Medical Imaging
11.4 Deep Learning in Medical Imaging
11.4.1 Image Categorisation
11.4.2 Object Classification
11.4.3 Organ or Region Detection
11.4.4 Data Mining
11.4.5 The Sign-up Process
11.5 Summary
References
12. Machine Learning Algorithms Used in Medical Field with a Case Study
12.1 Introduction
12.2 Machine Learning Algorithms
12.2.1 Supervised Learning
12.2.2 Unsupervised Learning
12.2.3 Reinforcement Learning
12.2.4 Semi-Supervised Learning
12.2.5 Regression Algorithms
12.2.6 Instance-based Algorithms
12.2.7 Regularization Algorithms
12.2.8 Decision Tree Algorithms
12.2.9 Bayesian Algorithms
12.2.10 Clustering Algorithms
12.2.11 Association Rule Learning Algorithms
12.2.12 Artificial Neural Network Algorithms
12.2.13 Deep Learning Algorithms
12.2.14 Dimensionality Reduction Algorithms
12.2.15 Ensemble Algorithms
12.3 ML Algorithms in Medical Diagnosis
12.4 ML Classifiers in Breast Cancer Diagnosis
12.4.1 Logistic Regression
12.4.2 K-Nearest Neighbor (k-NN) Algorithm
12.4.3 Support Vector Machine
12.4.4 Random Forest Classifier
12.4.5 Naive Bayes Classifier
12.4.6 Decision Tree Classifiers
12.4.7 Dimensionality Reduction Algorithms
12.5 Materials and Methods
12.6 Conclusion
References
13. Dual Customized U-Net-based Automated Diagnosis of Glaucoma
13.1 Introduction
13.2 Literature Review
13.3 Proposed Work
13.4 Performance Measures
13.5 Simulation Results
13.5.1 Optic Disc Segmentation
13.6 Conclusion
References
14. MuSCF-Net: Multi-scale, Multi-Channel Feature Network Using Resnet-based Attention Mechanism for Breast Histopathological Image Classification
14.1 Introduction
14.2 Related Studies
14.3 Contribution
14.4 Material and Methods
14.4.1 BREAKHIS Database
14.4.2 Methodology
14.4.3 Preprocessing
14.4.3.1 Patch Creation
14.4.3.2 Augmentation
14.4.3.3 MuSCF-Net Mechanism
14.4.3.3.1 Convolution of filter bank for feature extraction
14.4.3.3.2 Convolution Block Attention Module (CBMA) Integrated with ResBlock in ResNet
14.4.3.3.2.1 CBMA Integrated with ResBlockin Resnet
14.4.3.3.2.2 Channel Attention (CA) Module
14.4.3.3.2.3 Spatial Attention (SA) Module
14.4.3.3.2.4 ResBlock in Resnet [24]
14.4.3.3.2.5 Global Average Pooling Layer
14.4.3.3.2.6 Dropout Layer
14.4.3.3.2.7 Dense Block
14.4.4 Training Details
14.4.4.1 Adam Optimizer [37]
14.4.4.2 Activation Function
14.4.4.3 ReLU
14.4.4.4 Softmax Activation Function
14.4.4.5 Loss
14.5 Results and Discussion
14.6 Conclusion
References
15. Artificial Intelligence is Revolutionizing Cancer Research
15.1 Introduction
15.2 Development of Artificial Intelligence in Medical Research
15.3 AI in Different Cancer Treatment Modalities
15.3.1 Drug Development
15.3.2 Chemotherapy
15.3.3 Radiotherapy
15.3.4 Immunotherapy
15.3.5 Identifying Drug Targets
15.4 AI in Cancer Prediction at an Early Stage
15.5 Future Perspective in AI
15.6 Conclusion
References
16. Deep Learning to Diagnose Diseases and Security in 5G Healthcare Informatics
16.1 Introduction
16.2 Key Types of Learning Methods Used to Solve 5G Problems
16.2.1 Supervised Learning
16.2.2 Unsupervised Learning
16.2.3 Reinforcement Learning
16.3 Main Deep Learning Techniques Used in 5G Scenarios
16.3.1 Fully Connected Models
16.3.2 Recurrent Neural Networks
16.3.3 CNN
16.3.4 DBN
16.3.5 Autoencoder
16.3.6 Combining Models
16.4 Most Common Scenarios Used for 5G Assessment and Deep Learning Integration
16.5 Applications of Machine Learning and Deep Learning for 5G Security
16.6 Blockchain Technology in Healthcare
16.7 Evolution of Machine Learning in Disease Detection
16.7.1 Supervised Learning
16.7.1.1 K-Nearest Neighbour (KNN)
16.7.1.2 Support Vector Machine (SVM)
16.7.1.3 Decision Trees (DTs)
16.7.1.4 Classification and Regression Trees (CARTs)
16.7.1.5 Logistic Regression (LR)
16.7.1.6 Random Forest Algorithm (RFA)
16.7.1.7 Naive Bayes (NB)
16.7.1.8 Artificial Neural Network (ANN)
16.7.2 Unsupervised Learning
16.7.3 Semi-supervised Learning
16.7.4 Evolutionary Learning
16.7.5 Active Learning
16.7.6 Reinforcement Learning
16.7.7 Ensemble Learning
16.7.8 Deep Learning
16.7.9 Transfer Learning
16.7.9.1 Feature Extraction
16.7.9.2 Fine-tuning
16.8 Applications of Deep Learning in Disease Diagnosis
16.8.1 ML/DL in Healthcare: The Large Picture
16.8.2 A Look at the Healthcare Applications of ML and DL
16.9 Deep Learning in Disease Diagnosis: To Save Lives and Cuts Treatment Costs
16.9.1 Breast Cancer
16.9.2 Early Detection of Melanoma: Skin Cancer
16.9.3 Lung Cancer
16.9.4 Testing for Diabetic Retinopathy
16.9.5 Assessment of Cardiac Hazard from ECG Data
16.9.6 Using CT Scans of the Head to Detect Strokes Early
16.10 Benefits of Deep Learning
16.11 Scope of Deep Learning Techniques for Disease Diagnosis
16.12 A Deep Learning-Based Approach to Detect Neurodegenerative Diseases: Multiclass Classification (Case Study-1)
16.12.1 Material and Methods
16.12.1.1 ADPP Dataset Description
16.12.1.2 VGG 19 Architecture
16.12.2 Results and Discussion of the Above Discussed Framework
16.13 Pneumonia Detection using Deep Learning (Case Study-2)
16.13.1 Methodology
16.13.1.1 Structural Units
16.13.1.2 Architecture of CovXNet
16.13.1.3 Stacking of Multiple Networks
16.13.1.4 Transfer Learning Method of CovXNet for New Corona Virus Data
16.13.1.5 Network Training and Optimisation
16.13.2 Results and Discussions
16.13.2.1 Datasets
16.13.2.2 Evaluation of Performance
16.14 Early Detection of Deep Learning-based Diabetic Retinopathy (Case Study-3)
16.14.1 Datasets Used
16.14.2 Metric Assessment
16.14.2.1 Quadratic Weighted Kappa (QWK)
16.14.2.2 Intuition of Cohen's Kappa
16.14.2.3 Quadratic Weight Intuition in Ordinary Classes — Quadratic Weighted Kappa (QWK)
16.14.3 Method
16.14.3.1 Image Pre-processing and Augmentations
16.14.3.2 Network Architecture
16.14.3.3 Training Process
16.14.4 Results
16.14.4.1 Model Evaluation on Test Data
16.14.4.2 Other Transfer Learning Models
16.15 Conclusion
References
17. New Approaches in Machine-based Image Analysis for Medical Oncology
17.1 Introduction
17.2 Classical Methods
17.3 Machine Learning Methods in Oncology
17.3.1 Supervised Learning
17.3.1.1 Support Vector Machine
17.3.1.2 Logistic or Linear Regression (LR)
17.3.1.3 Decision Tree
17.3.1.4 Random Forest Algorithm (RF)
17.3.1.5 Naive Bayes
17.3.1.6 K-Nearest Neighbour
17.3.1.7 Artificial Neural Network
17.3.2 Unsupervised Learning
17.3.2.1 K-means Clustering
17.3.2.2 Principle Component Analysis
17.3.2.3 Independent Component Analysis
17.3.2.4 Autoencoders
17.3.2.5 Singular Value Decomposition
17.3.3 Reinforcement Learning (RL)
17.4 Application of ML in Oncology
17.4.1 Brain Oncology
17.4.2 Skin Oncology
17.4.3 Breast Cancer Prognosis Prediction
17.4.4 ML in Lung Oncology
17.4.5 Gastric Oncology
17.5 Discussion
17.5.1 ML Program Performance Analysis
17.5.2 Pros and Cons of ML Algorithm
17.5.3 ML in Cancer Staging
17.5.4 Predicting and Evaluating Treatment Response
17.6 Conclusion
References
18. Performance Analysis of Deep Convolutional Neural Networks for Diagnosing COVID-19: Data to Deployment
18.1 Introduction
18.2 Literature Review
18.3 The Attributes of the Dataset and Visualizations to Interpret the Data
18.4 The Model Formulation to Classify the Data to Diagnose COVID-19
18.4.1 InceptionResNetV2
18.4.2 ResNet152V2
18.4.3 Xception
18.4.4 DenseNet201
18.5 Loss Function: Categorical Cross Entropy
18.6 Evaluation Metrics and Results
18.7 Model Deployment
18.7.1 Designing the Website
18.7.2 An Overview of Deployment
18.7.3 Working of Website
18.8 Conclusion
References
19. Stacked Auto Encoder Deep Neural Network with Principal Components Analysis for Identification of Chronic Kidney Disease
19.1 Introduction
19.2 Methodology
19.2.1 Stacked Auto-encoder Deep Neural Network
19.2.2 Principal Component Analysis (PCA)
19.3 Result and Discussion
19.4 Conclusion
References
Index