Machine Learning for Healthcare Applications

When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment.

Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning.

This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.

SACHI NANDAN MOHANTY received his PhD from IIT Kharagpur in 2015. He has recently joined as an associate professor in the Department of Computer Science & Engineering at ICFAI Foundation for Higher Education Hyderabad. His research areas include data mining, big data analysis, cognitive science, fuzzy decision making, brain-computer interface, and computational intelligence. He has published 20 SCI journal articles and has authored/edited 7 books.

G. NALINIPRIYA is a professor in the Department of Information Technology, Anna University, Chennai where she also obtained her PhD. She has more than 23 years of experience in the field of teaching, industry and research and her interests include artificial intelligence, machine learning, data science and cloud security.

OM PRAKASH JENA is an assistant professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha. He has 10 years of teaching and research experience and has published several technical papers in international journals/conferences/edited books. His current research interests include pattern recognition, cryptography, network security, soft computing, data analytics and machine automation.

ACHYUTH SARKAR received his PhD in Computer Science and Engineering from the National Institute of Technology, Arunachal Pradesh in 2019. He has teaching experience of more than 10 years.

Preface xvii

PART 1: INTRODUCTION TO INTELLIGENT HEALTHCARE SYSTEMS 1

1 INNOVATION ON MACHINE LEARNING IN HEALTHCARE SERVICES—AN INTRODUCTION 3

Parthasarathi Pattnayak and Om Prakash Jena

1.1 Introduction 3

1.2 Need for Change in Healthcare 5

1.3 Opportunities of Machine Learning in Healthcare 6

1.4 Healthcare Fraud 7

1.4.1 Sorts of Fraud in Healthcare 7

1.4.2 Clinical Service Providers 8

1.4.3 Clinical Resource Providers 8

1.4.4 Protection Policy Holders 8

1.4.5 Protection Policy Providers 9

1.5 Fraud Detection and Data Mining in Healthcare 9

1.5.1 Data Mining Supervised Methods 10

1.5.2 Data Mining Unsupervised Methods 10

1.6 Common Machine Learning Applications in Healthcare 10

1.6.1 Multimodal Machine Learning for Data Fusion in Medical Imaging 11

1.6.2 Machine Learning in Patient Risk Stratification 11

1.6.3 Machine Learning in Telemedicine 11

1.6.4 AI (ML) Application in Sedate Revelation 12

1.6.5 Neuroscience and Image Computing 12

1.6.6 Cloud Figuring Systems in Building AI-Based Healthcare 12

1.6.7 Applying Internet of Things and Machine-Learning for Personalized Healthcare 12

1.6.8 Machine Learning in Outbreak Prediction 13

1.7 Conclusion 13

References 14

PART 2: MACHINE LEARNING/DEEP LEARNING-BASED MODEL DEVELOPMENT 17

2 A FRAMEWORK FOR HEALTH STATUS ESTIMATION BASED ON DAILY LIFE ACTIVITIES DATA USING MACHINE LEARNING TECHNIQUES 19

Tene Ramakrishnudu, T. Sai Prasen and V. Tharun Chakravarthy

2.1 Introduction 19

2.1.1 Health Status of an Individual 19

2.1.2 Activities and Measures of an Individual 20

2.1.3 Traditional Approach to Predict Health Status 20

2.2 Background 20

2.3 Problem Statement 21

2.4 Proposed Architecture 22

2.4.1 Pre-Processing 22

2.4.2 Phase-I 23

2.4.3 Phase-II 23

2.4.4 Dataset Generation 23

2.4.4.1 Rules Collection 23

2.4.4.2 Feature Selection 24

2.4.4.3 Feature Reduction 24

2.4.4.4 Dataset Generation From Rules 24

2.4.4.5 Example 24

2.4.5 Pre-Processing 26

2.5 Experimental Results 27

2.5.1 Performance Metrics 27

2.5.1.1 Accuracy 27

2.5.1.2 Precision 28

2.5.1.3 Recall 28

2.5.1.4 F1-Score 30

2.6 Conclusion 31

References 31

3 STUDY OF NEUROMARKETING WITH EEG SIGNALS AND MACHINE LEARNING TECHNIQUES 33

S. Pal, P. Das, R. Sahu and S.R. Dash

3.1 Introduction 34

3.1.1 Why BCI 34

3.1.2 Human–Computer Interfaces 34

3.1.3 What is EEG 35

3.1.4 History of EEG 35

3.1.5 About Neuromarketing 35

3.1.6 About Machine Learning 36

3.2 Literature Survey 36

3.3 Methodology 45

3.3.1 Bagging Decision Tree Classifier 45

3.3.2 Gaussian Naïve Bayes Classifier 45

3.3.3 Kernel Support Vector Machine (Sigmoid) 45

3.3.4 Random Decision Forest Classifier 46

3.4 System Setup & Design 46

3.4.1 Pre-Processing & Feature Extraction 47

3.4.1.1 Savitzky–Golay Filter 47

3.4.1.2 Discrete Wavelet Transform 48

3.4.2 Dataset Description 49

3.5 Result 49

3.5.1 Individual Result Analysis 49

3.5.2 Comparative Results Analysis 52

3.6 Conclusion 53

References 54

4 AN EXPERT SYSTEM-BASED CLINICAL DECISION SUPPORT SYSTEM FOR HEPATITIS-B PREDICTION & DIAGNOSIS 57

Niranjan Panigrahi, Ishan Ayus and Om Prakash Jena

4.1 Introduction 57

4.2 Outline of Clinical DSS 59

4.2.1 Preliminaries 59

4.2.2 Types of Clinical DSS 60

4.2.3 Non-Knowledge-Based Decision Support System (NK-DSS) 60

4.2.4 Knowledge-Based Decision Support System (K-DSS) 62

4.2.5 Hybrid Decision Support System (H-DSS) 64

4.2.6 DSS Architecture 64

4.3 Background 65

4.4 Proposed Expert System-Based CDSS 65

4.4.1 Problem Description 65

4.4.2 Rules Set & Knowledge Base 66

4.4.3 Inference Engine 66

4.5 Implementation & Testing 66

4.6 Conclusion 73

References 73

5 DEEP LEARNING ON SYMPTOMS IN DISEASE PREDICTION 77

Sheikh Raul Islam, Rohit Sinha, Santi P. Maity and Ajoy Kumar Ray

5.1 Introduction 77

5.2 Literature Review 78

5.3 Mathematical Models 79

5.3.1 Graphs and Related Terms 80

5.3.2 Deep Learning in Graph 80

5.3.3 Network Embedding 80

5.3.4 Graph Neural Network 81

5.3.5 Graph Convolution Network 82

5.4 Learning Representation From DSN 82

5.4.1 Description of the Proposed Model 83

5.4.2 Objective Function 84

5.5 Results and Discussion 84

5.5.1 Description of the Dataset 85

5.5.2 Training Progress 85

5.5.3 Performance Comparisons 86

5.6 Conclusions and Future Scope 86

References 87

6 INTELLIGENT VISION-BASED SYSTEMS FOR PUBLIC SAFETY AND PROTECTION VIA MACHINE LEARNING TECHNIQUES 89

Rajitha B.

6.1 Introduction 89

6.1.1 Problems Intended in Video Surveillance Systems 90

6.1.2 Current Developments in This Area 91

6.1.3 Role of AI in Video Surveillance Systems 91

6.2 Public Safety and Video Surveillance Systems 92

6.2.1 Offline Crime Prevention 92

6.2.2 Crime Prevention and Identification via Apps 92

6.2.3 Crime Prevention and Identification via CCTV 92

6.3 Machine Learning for Public Safety 94

6.3.1 Abnormality Behavior Detection via Deep Learning 95

6.3.2 Video Analytics Methods for Accident Classification/Detection 97

6.3.3 Feature Selection and Fusion Methods 98

6.4 Securing the CCTV Data 99

6.4.1 Image/Video Security Challenges 99

6.4.2 Blockchain for Image/Video Security 99

6.5 Conclusion 99

References 100

7 SEMANTIC FRAMEWORK IN HEALTHCARE 103

Sankar Pariserum Perumal, Ganapathy Sannasi, Selvi M. and Kannan Arputharaj

7.1 Introduction 103

7.2 Semantic Web Ontology 104

7.3 Multi-Agent System in a Semantic Framework 106

7.3.1 Existing Healthcare Semantic Frameworks 107

7.3.1.1 AOIS 107

7.3.1.2 SCKE 108

7.3.1.3 MASE 109

7.3.1.4 MET4 110

7.3.2 Proposed Multi-Agent-Based Semantic Framework for Healthcare Instance Data 111

7.3.2.1 Data Dictionary 111

7.3.2.2 Mapping Database 112

7.3.2.3 Decision Making Ontology 113

7.3.2.4 STTL and SPARQL-Based RDF Transformation 115

7.3.2.5 Query Optimizer Agent 116

7.3.2.6 Semantic Web Services Ontology 116

7.3.2.7 Web Application User Interface and Customer Agent 116

7.3.2.8 Translation Agent 117

7.3.2.9 RDF Translator 117

7.4 Conclusion 118

References 119

8 DETECTION, PREDICTION & INTERVENTION OF ATTENTION DEFICIENCY IN THE BRAIN USING TDCS 121

Pallabjyoti Kakoti, Rissnalin Syiemlieh and Eeshankur Saikia

8.1 Introduction 121

8.2 Materials & Methods 123

8.2.1 Subjects and Experimental Design 123

8.2.2 Data Pre-Processing & Statistical Analysis 125

8.2.3 Extracting Singularity Spectrum from EEG 126

8.3 Results & Discussion 126

8.4 Conclusion 132

Acknowledgement 133

References 133

9 DETECTION OF ONSET AND PROGRESSION OF OSTEOPOROSIS USING MACHINE LEARNING 137

Shilpi Ruchi Kerketta and Debalina Ghosh

9.1 Introduction 137

9.1.1 Measurement Techniques of BMD 138

9.1.2 Machine Learning Algorithms in Healthcare 138

9.1.3 Organization of Chapter 139

9.2 Microwave Characterization of Human Osseous Tissue 139

9.2.1 Frequency-Domain Analysis of Human Wrist Sample 140

9.2.2 Data Collection and Analysis 141

9.3 Prediction Model of Osteoporosis Using Machine Learning Algorithms 144

9.3.1 K-Nearest Neighbor (KNN) 144

9.3.2 Decision Tree 145

9.3.3 Random Forest 145

9.4 Conclusion 148

Acknowledgment 148

References 148

10 APPLICATIONS OF MACHINE LEARNING IN BIOMEDICAL TEXT PROCESSING AND FOOD INDUSTRY 151

K. Paramesha, Gururaj H.L. and Om Prakash Jena

10.1 Introduction 152

10.2 Use Cases of AI and ML in Healthcare 153

10.2.1 Speech Recognition (SR) 153

10.2.2 Pharmacovigilance and Adverse Drug Effects (ADE) 153

10.2.3 Clinical Imaging and Diagnostics 153

10.2.4 Conversational AI in Healthcare 154

10.3 Use Cases of AI and ML in Food Technology 154

10.3.1 Assortment of Vegetables and Fruits 154

10.3.2 Personal Hygiene 154

10.3.3 Developing New Products 155

10.3.4 Plant Leaf Disease Detection 156

10.3.5 Face Recognition Systems for Domestic Cattle 156

10.3.6 Cleaning Processing Equipment 157

10.4 A Case Study: Sentiment Analysis of Drug Reviews 158

10.4.1 Dataset 159

10.4.2 Approaches for Sentiment Analysis on Drug Reviews 159

10.4.3 BoW and TF-IDF Model 160

10.4.4 Bi-LSTM Model 160

10.4.4.1 Word Embedding 160

10.4.5 Deep Learning Model 161

10.5 Results and Analysis 164

10.6 Conclusion 165

References 166

11 COMPARISON OF MOBILENET AND RESNET CNN ARCHITECTURES IN THE CNN-BASED SKIN CANCER CLASSIFIER MODEL 169

Subasish Mohapatra, N.V.S. Abhishek, Dibyajit Bardhan, Anisha Ankita Ghosh and Shubhadarshinin Mohanty

11.1 Introduction 169

11.2 Our Skin Cancer Classifier Model 171

11.3 Skin Cancer Classifier Model Results 172

11.4 Hyperparameter Tuning and Performance 174

11.4.1 Hyperparameter Tuning of MobileNet-Based CNN Model 175

11.4.2 Hyperparameter Tuning of ResNet50-Based CNN Model 175

11.4.3 Table Summary of Hyperparameter Tuning Results 176

11.5 Comparative Analysis and Results 176

11.5.1 Training and Validation Loss 177

11.5.1.1 MobileNet 177

11.5.1.2 ResNet50 177

11.5.1.3 Inferences 177

11.5.2 Training and Validation Categorical Accuracy 178

11.5.2.1 MobileNet 178

11.5.2.2 ResNet50 178

11.5.2.3 Inferences 178

11.5.3 Training and Validation Top 2 Accuracy 179

11.5.3.1 MobileNet 179

11.5.3.2 ResNet50 179

11.5.3.3 Inferences 180

11.5.4 Training and Validation Top 3 Accuracy 180

11.5.4.1 MobileNet 180

11.5.4.2 ResNet50 180

11.5.4.3 Inferences 181

11.5.5 Confusion Matrix 181

11.5.5.1 MobileNet 181

11.5.5.2 ResNet50 181

11.5.5.3 Inferences 182

11.5.6 Classification Report 182

11.5.6.1 MobileNet 182

11.5.6.2 ResNet50 182

11.5.6.3 Inferences 183

11.5.7 Last Epoch Results 183

11.5.7.1 MobileNet 183

11.5.7.2 ResNet50 183

11.5.7.3 Inferences 184

11.5.8 Best Epoch Results 184

11.5.8.1 MobileNet 184

11.5.8.2 ResNet50 184

11.5.8.3 Inferences 184

11.5.9 Overall Comparative Analysis 184

11.6 Conclusion 185

References 185

12 DEEP LEARNING-BASED IMAGE CLASSIFIER FOR MALARIA CELL DETECTION 187

Alok Negi, Krishan Kumar and Prachi Chauhan

12.1 Introduction 187

12.2 Related Work 189

12.3 Proposed Work 190

12.3.1 Dataset Description 191

12.3.2 Data Pre-Processing and Augmentation 191

12.3.3 CNN Architecture and Implementation 192

12.4 Results and Evaluation 194

12.5 Conclusion 196

References 197

13 PREDICTION OF CHEST DISEASES USING TRANSFER LEARNING 199

S. Baghavathi Priya, M. Rajamanogaran and S. Subha

13.1 Introduction 199

13.2 Types of Diseases 200

13.2.1 Pneumothorax 200

13.2.2 Pneumonia 200

13.2.3 Effusion 200

13.2.4 Atelectasis 201

13.2.5 Nodule and Mass 202

13.2.6 Cardiomegaly 202

13.2.7 Edema 202

13.2.8 Lung Consolidation 202

13.2.9 Pleural Thickening 202

13.2.10 Infiltration 202

13.2.11 Fibrosis 203

13.2.12 Emphysema 203

13.3 Diagnosis of Lung Diseases 204

13.4 Materials and Methods 204

13.4.1 Data Augmentation 206

13.4.2 CNN Architecture 206

13.4.3 Lung Disease Prediction Model 207

13.5 Results and Discussions 208

13.5.1 Implementation Results Using ROC Curve 209

13.6 Conclusion 210

References 212

14 EARLY STAGE DETECTION OF LEUKEMIA USING ARTIFICIAL INTELLIGENCE 215

Neha Agarwal and Piyush Agrawal

14.1 Introduction 215

14.1.1 Classification of Leukemia 216

14.1.1.1 Acute Lymphocytic Leukemia 216

14.1.1.2 Acute Myeloid Leukemia 216

14.1.1.3 Chronic Lymphocytic Leukemia 216

14.1.1.4 Chronic Myeloid Leukemia 216

14.1.2 Diagnosis of Leukemia 216

14.1.3 Acute and Chronic Stages of Leukemia 217

14.1.4 The Role of AI in Leukemia Detection 217

14.2 Literature Review 219

14.3 Proposed Work 220

14.3.1 Modules Involved in Proposed Methodology 221

14.3.2 Flowchart 222

14.3.3 Proposed Algorithm 223

14.4 Conclusion and Future Aspects 223

References 223

PART 3: INTERNET OF MEDICAL THINGS (IOMT) FOR HEALTHCARE 225

15 IOT APPLICATION IN INTERCONNECTED HOSPITALS 227

Subhra Debdas, Chinmoy Kumar Panigrahi, Priyasmita Kundu, Sayantan Kundu and Ramanand Jha

15.1 Introduction 228

15.2 Networking Systems Using IoT 229

15.3 What are Smart Hospitals? 233

15.3.1 Environment of a Smart Hospital 234

15.4 Assets 236

15.4.1 Overview of Smart Hospital Assets 236

15.4.2 Exigency of Automated Healthcare Center Assets 239

15.5 Threats 241

15.5.1 Emerging Vulnerabilities 241

15.5.2 Threat Analysis 244

15.6 Conclusion 246

References 246

16 REAL TIME HEALTH MONITORING USING IOT WITH INTEGRATION OF MACHINE LEARNING APPROACH 249

K.G. Maheswari, G. Nalinipriya, C. Siva and A. Thilakesh Raj

16.1 Introduction 250

16.2 Related Work 250

16.3 Existing Healthcare Monitoring System 251

16.4 Methodology and Data Analysis 251

16.5 Proposed System Architecture 252

16.6 Machine Learning Approach 252

16.6.1 Multiple Linear Regression Algorithm 253

16.6.2 Random Forest Algorithm 253

16.6.3 Support Vector Machine 253

16.7 Work Flow of the Proposed System 253

16.8 System Design of Health Monitoring System 256

16.9 Use Case Diagram 257

16.10 Conclusion 258

References 259

PART 4: MACHINE LEARNING APPLICATIONS FOR COVID-19 261

17 SEMANTIC AND NLP-BASED RETRIEVAL FROM COVID-19 ONTOLOGY 263

Ramar Kaladevi and Appavoo Revathi

17.1 Introduction 263

17.2 Related Work 264

17.3 Proposed Retrieval System 266

17.3.1 Why Ontology? 266

17.3.2 Covid Ontology 266

17.3.3 Information Retrieval From Ontology 269

17.3.4 Query Formulation 272

17.3.5 Retrieval From Knowledgebase 272

17.4 Conclusion 273

References 273

18 SEMANTIC BEHAVIOR ANALYSIS OF COVID-19 PATIENTS: A COLLABORATIVE FRAMEWORK 277

Amlan Mohanty, Debasish Kumar Mallick, Shantipriya Parida and Satya Ranjan Dash

18.1 Introduction 278

18.2 Related Work 280

18.2.1 Semantic Analysis and Topic Discovery of Alcoholic Patients From Social Media Platforms 280

18.2.2 Sentiment Analysis of Tweets From Twitter Handles of the People of Nepal in Response to the COVID-19 Pandemic 280

18.2.3 Study of Sentiment Analysis and Analyzing Scientific Papers 280

18.2.4 Informatics and COVID-19 Research 281

18.2.5 COVID-19 Outbreak in the World and Twitter Sentiment Analysis 281

18.2.6 LDA Topic Modeling on Twitter to Study Public Discourse and Sentiment During the Coronavirus Pandemic 281

18.2.7 The First Decade of Research on Sentiment Analysis 282

18.2.8 Detailed Survey on the Semantic Analysis Techniques for NLP 282

18.2.9 Understanding Text Semantics With LSA 282

18.2.10 Analyzing Suicidal Tendencies With Semantic Analysis Using Social Media 283

18.2.11 Analyzing Public Opinion on BREXIT Using Sentiment Analysis 283

18.2.12 Prediction of Indian Elections Using NLP and Decision Tree 283

18.3 Methodology 283

18.4 Conclusion 286

References 287

19 COMPARATIVE STUDY OF VARIOUS DATA MINING TECHNIQUES TOWARDS ANALYSIS AND PREDICTION OF GLOBAL COVID-19 DATASET 289

Sachin Kamley

19.1 Introduction 289

19.2 Literature Review 290

19.3 Materials and Methods 292

19.3.1 Dataset Collection 292

19.3.2 Support Vector Machine (SVM) 292

19.3.3 Decision Tree (DT) 294

19.3.4 K-Means Clustering 294

19.3.5 Back Propagation Neural Network (BPNN) 295

19.4 Experimental Results 296

19.5 Conclusion and Future Scopes 305

References 306

20 AUTOMATED DIAGNOSIS OF COVID-19 USING REINFORCED LUNG SEGMENTATION AND CLASSIFICATION MODEL 309

J. Shiny Duela and T. Illakiya

20.1 Introduction 309

20.2 Diagnosis of COVID-19 310

20.2.1 Pre-Processing of Lung CT Image 310

20.2.2 Lung CT Image Segmentation 311

20.2.3 ROI Extraction 311

20.2.4 Feature Extraction 311

20.2.5 Classification 311

20.3 Genetic Algorithm (GA) 311

20.3.1 Operators of GA 312

20.3.2 Applications of GA 312

20.4 Related Works 313

20.5 Challenges in GA 314

20.6 Challenges in Lung CT Segmentation 314

20.7 Proposed Diagnosis Framework 314

20.7.1 Image Pre-Processing 315

20.7.2 Proposed Image Segmentation Technique 315

20.7.3 ROI Segmentation 318

20.7.4 Feature Extraction 318

20.7.5 Modified GA Classifier 318

20.7.5.1 Gaussian Type—II Fuzzy in Classification 318

20.7.5.2 Classifier Algorithm 319

20.8 Result Discussion 319

20.9 Conclusion 321

References 321

PART 5: CASE STUDIES OF APPLICATION AREAS OF MACHINE LEARNING IN HEALTHCARE SYSTEM 323

21 FUTURE OF TELEMEDICINE WITH ML: BUILDING A TELEMEDICINE FRAMEWORK FOR LUNG SOUND DETECTION 325

Sudhansu Shekhar Patra, Nitin S. Goje, Kamakhya Narain Singh, Kaish Q. Khan, Deepak Kumar, Madhavi and Kumar Ashutosh Sharma

21.1 Introduction 325

21.1.1 Monitoring the Remote Patient 326

21.1.2 Intelligent Assistance for Patient Diagnosis 326

21.1.3 Fasten Electronic Health Record Retrieval Process 326

21.1.4 Collaboration Increases Among Healthcare Practitioners 326

21.2 Related Work 327

21.3 Strategic Model for Telemedicine 328

21.4 Framework for Lung Sound Detection in Telemedicine 330

21.4.1 Data Collection 330

21.4.2 Pre-Processing of Data 331

21.4.3 Feature Extraction 331

21.4.3.1 MFCC 331

21.4.3.2 Lung Sounds Using Multi Resolution DWT 332

21.4.4 Classification 334

21.4.4.1 Correlation Coefficient for Feature Selection (CFS) 334

21.4.4.2 Symmetrical Uncertainty 334

21.4.4.3 Gain Ratio 335

21.4.4.4 Modified RF Classification Architecture 335

21.5 Experimental Analysis 335

21.6 Conclusion 340

References 340

22 A LIGHTWEIGHT CONVOLUTIONAL NEURAL NETWORK MODEL FOR TUBERCULOSIS BACILLI DETECTION FROM MICROSCOPIC SPUTUM SMEAR IMAGES 343

Rani Oomman Panicker, S.J. Pawan, Jeny Rajan and M.K. Sabu

22.1 Introduction 343

22.2 Literature Review 345

22.3 Proposed Work 346

22.4 Experimental Results and Discussion 349

22.5 Conclusion 350

References 350

23 ROLE OF MACHINE LEARNING AND TEXTURE FEATURES FOR THE DIAGNOSIS OF LARYNGEAL CANCER 353

Vibhav Prakash Singh and Ashish Kumar Maurya

23.1 Introduction 353

23.2 Clinically Correlated Texture Features 358

23.2.1 Texture-Based LBP Descriptors 358

23.2.2 GLCM Features 358

23.2.3 Statistical Features 359

23.3 Machine Learning Techniques 359

23.3.1 Support Vector Machine (SVM) 359

23.3.2 k-NN (k-Nearest Neighbors) 360

23.3.3 Random Forest (RF) 361

23.3.4 Naïve Bayes 361

23.4 Result Analysis and Discussions 361

23.5 Conclusions 366

References 366

24 ANALYSIS OF MACHINE LEARNING TECHNOLOGIES FOR THE DETECTION OF DIABETIC RETINOPATHY 369

Biswabijayee Chandra Sekhar Mohanty, Sonali Mishra and Sambit Kumar Mishra

24.1 Introduction 369

24.2 Related Work 370

24.2.1 Pre-Processing of Image 371

24.2.2 Diabetic Retinopathy Detection 372

24.2.3 Grading of DR 374

24.3 Dataset Used 374

24.3.1 DIARETDB1 374

24.3.2 Diabetic-Retinopathy-Detection Dataset 376

24.4 Methodology Used 377

24.4.1 Pre-Processing 377

24.4.2 Segmentation 377

24.4.3 Feature Extraction 378

24.4.4 Classification 378

24.5 Analysis of Results and Discussion 379

24.6 Conclusion 380

References 381

Index 383