Computational Intelligence for Oncology and Neurological Disorders: Current Practices and Future Directions (Chapman & Hall/CRC Computational Biology Series)

With the advent of computational intelligence-based approaches, such as bio-inspired techniques, and the availability of clinical data from various complex experiments, medical consultants, researchers, neurologists, and oncologists, there is huge scope for CI-based applications in medical oncology and neurological disorders. This book focuses on interdisciplinary research in this field, bringing together medical practitioners dealing with neurological disorders and medical oncology along with CI investigators. The book collects high-quality original contributions, containing the latest developments or applications of practical use and value, presenting interdisciplinary research and review articles in the field of intelligent systems for computational oncology and neurological disorders. Drawing from work across computer science, physics, mathematics, medical science, psychology, cognitive science, oncology, and neurobiology among others, it combines theoretical, applied, computational, experimental, and clinical research. It will be of great interest to any neurology or oncology researchers focused on computational approaches. Cover Half Title Series Page Title Page Copyright Page Table of Contents Preface About the Editors List of Contributors Part 1 Neurological Disorders Chapter 1 Advancements in AI for Mental Health: Exploring ASD, ADHD and Schizophrenia, Video Datasets, and Future Directions 1.1 Introduction 1.2 Artificial Intelligence (AI) Research in Mental Health 1.2.1 Convoluted Nature of Mental Health Disorders 1.2.2 AI as a Transformative Force 1.2.3 Bridging the Gap: Integrating Technology and Mental Healthcare 1.3 The Enigma of Neurological Disorders 1.3.1 Autism Spectrum Disorder (ASD): Unravelling Social Perceptions 1.3.2 Attention-Deficit Hyperactivity Disorder (ADHD): Balancing Focus and Impulsivity 1.3.3 Schizophrenia: Distorted Realities and Emotional Upheaval 1.4 The Role of AI in Advancing Research on Mental Health 1.4.1 Understanding Artificial Intelligence 1.4.2 Advanced Technological Methods for the Analysis of Neurological Disorders 1.5 The Intricacies of AI and Datasets in Neurological and Psychological Disorder Analysis 1.5.1 Analysis of ASD, ADHD, and Schizophrenia Using Multimodal Datasets 1.5.2 Analysis of ASD, ADHD, and Schizophrenia Using Video Datasets 1.6 Ethical Considerations and Future Directions 1.6.1 Mindful Integration of AI and Ethical Deliberations 1.6.2 Envisioning a Future Empowered by AI for Mental Health Reference list Chapter 2 Blockchain Applications in Neurological Disorders and Oncology 2.1 Introduction 2.2 Fundamentals of Blockchain Technology 2.2.1 Its History 2.2.2 How Does a Blockchain Work? 2.3 Blockchain Applications in Neurological Disorders and Oncology 2.3.1 Blockchain-Based Management of Electronic Health Record (EHR) and Health Information Exchange (HIE) 2.3.2 Blockchain-Based Management of Medical Supply Chain 2.3.3 Blockchain-Based Medical Research and Pharmacological Studies 2.3.4 Controllable IoMT-Based Medical Devices via Blockchain 2.3.5 Blockchain-Based Genomic Research 2.4 Conclusion Acknowledgements References Chapter 3 Deep Scattering Wavelet Network and Marine Predators Algorithm-Based Stuttering Disfluency Detection 3.1 Introduction 3.2 Literature Survey 3.3 Proposed Scattering Wavelet Network-Based Stuttering Disfluency Detection Algorithm 3.4 Feature Extraction and Feature Selection 3.4.1 Scattering Wavelet Network (ScatNet) 3.4.2 Marine Predator Algorithm 3.5 Experimental Results and Discussions 3.6 Marine Predator Optimization-based Feature Selection Analysis 3.7 Conclusion References Chapter 4 AI in Neurological Disorders: A Systematic Review 4.1 Introduction 4.1.1 Objective of the Work 4.1.2 Organisation of the Chapter 4.2 Artificial Intelligence Techniques 4.3 Applications of AI 4.3.1 Medical Image File Formats 4.3.2 Measures of Brain Activity 4.4 Neurological Disorders and their Types 4.5 AI in the Prediction of Neurological Disorders 4.6 Conclusion and Future Scope References Chapter 5 Malformation Risk Prediction with Machine Learning Modelling for Pregnant Women with Epilepsy 5.1 Introduction 5.2 Materials and Methods 5.3 Dataset and Framework of MCM Prediction 5.4 Pre-processing and Balancing the Data 5.5 Machine Learning Models and Experimental Analysis 5.5.1 Logistic Regression 5.5.2 Naïve Bayes Classifier (NBC) 5.5.3 Decision Tree 5.5.4 Adaboost 5.5.5 Random Forest (RF) 5.5.6 Stacking 5.6 Performance Metrics 5.7 Results 5.8 Discussion 5.9 Future Scope 5.9.1 Validation and Generalization 5.9.2 Refinement and Enhancement 5.9.3 Personalized Medicine 5.9.4 Real-Time Risk Assessment 5.9.5 Patient Education and Counseling 5.9.6 Longitudinal Studies 5.9.7 International Collaboration 5.10 Conclusion References Chapter 6 The Computational Techniques in Mutational Disease Prediction: A Comprehensive and Comparative Review 6.1 Introduction 6.1.1 Biological Character Sequence 6.1.2 Numerical Representation 6.1.3 Exon Detection in DNA 6.1.4 The Purpose of Exon Detection in DNA 6.1.5 Mutation 6.1.6 Types of Mutations 6.1.7 The Mutational Disease 6.1.8 Mutational Disease Analysis 6.1.9 The Mutational Disease Prediction 6.2 Literature Survey 6.2.1 Evaluation Parameters 6.3 Computational Techniques in Mutational Disease Prediction 6.3.1 FLANN-Based Levenberg Marquardt Adaptive Algorithm in Discrimination of Diseased and Healthy Gene 6.3.2 A Hybrid Deep-Learning Approach for COVID–19 Detection Based on Genomic Image Processing Techniques 6.3.3 Comprehensive Evaluation of Computational Methods for Predicting Cancer Driver Genes 6.3.4 An Adaptive Neural Network Model for Predicting Breast Cancer Disease in Mapped Nucleotide Sequences 6.3.5 Signal Processing Approaches for Encoded Protein Sequences in Gynaecological Cancer Hotspot Prediction: A Review 6.3.6 Modified Gabor Wavelet Transform in Prediction of Cancerous Genes 6.4 Case Study and Discussion 6.4.1 Goat Dataset 6.4.2 The Computational Techniques 6.5 Conclusions 6.6 Future Aspects of the Work 6.7 Open Access Database References Chapter 7 Comparative Analysis of U-Net and DeepLab for Accurate Brain MRI Segmentation 7.1 Introduction 7.2 Related Work 7.3 Brain MRI Segmentation Dataset 7.4 Experimental Models 7.4.1 U-Net 7.4.2 DeepLab 7.5 Explanation of Each Phase of the Experiment Analysis Process 7.5.1 Helper Function 7.5.2 Uniform Training Duration 7.5.3 U-Net Model 7.5.4 DeepLab V 7.5.5 DeepLab V 7.5.6 DeepLab V 7.5.7 DeepLab V3+ 7.5.8 Training Loss and Metrics 7.5.9 Training Time 7.5.10 Testing Metrics 7.5.11 Experimental Results and Discussion 7.6 Limitations 7.7 Conclusions and Future Scope References Chapter 8 A Comprehensive Review on Depression Detection Based on Text from Social Media Posts 8.1 Introduction 8.1.1 Objectives and Research Questions 8.2 Searching Strategies 8.2.1 Search Source 8.2.2 Search Terms 8.2.3 Dataset Extraction 8.3 Related Work 8.3.1 Application of Twitter Data in Mental Health with Different Approaches 8.3.2 Application of Facebook Data in Mental Health with Different Approaches 8.3.3 Application of Reddit Data in Mental Health with Different Approaches 8.3.4 Challenges 8.4 Methodology 8.4.1 Data Collection 8.4.2 Text-Based Approaches for Early Depression Detection 8.4.3 Performance Measures 8.4.4 BERT Classification 8.5 Conclusion References Part 2 Oncology Chapter 9 Artificial Intelligence in Radiation Oncology 9.1 Introduction 9.2 AI in Treatment Planning 9.3 AI in Image Analysis 9.3.1 Radiomics 9.3.2 Deep Learning for Image Classification 9.4 AI in Outcome Prediction 9.4.1 Predictive Models 9.4.2 Treatment Response Monitoring 9.5 AI in Adaptive Therapy 9.5.1 Online Adaptive Radiotherapy 9.5.2 Response-Driven Personalization 9.6 Challenges and Opportunities 9.6.1 Data Quality and Diversity 9.6.2 Interpretability and Explainability 9.6.3 Validation and Regulatory Approval 9.6.4 Integration into Clinical Workflow 9.6.5 Ethical Considerations 9.6.6 Collaborative Research and Education 9.6.7 Bias and Fairness 9.7 Conclusion References Chapter 10 A Comprehensive Overview of AI Applications in Radiation Oncology 10.1 Introduction 10.2 Background 10.3 Electronic Medical Records 10.3.1 AI for Management Purposes in EMR Systems 10.3.2 AI for Predictive Purposes in EMR Systems 10.4 AI for Image Segmentation and Contouring 10.5 AI in Image Registration 10.6 AI for Motion Tracking 10.7 AI for Treatment Plan Optimization and Personalized Therapy 10.8 AI and Regulatory Considerations in Radiation Oncology 10.9 Case Studies 10.10 Challenges and Future Directions References Chapter 11 Melanoma Skin Cancer Identification on Embedded Devices Using Digital Hair Removal and Transfer Learning 11.1 Introduction 11.2 Literature Review 11.3 Methodology 11.3.1 ISIC Dataset 11.3.2 Preprocessing 11.3.3 Digital Hair Removal (HDR) Algorithm for Digital Shaving 11.3.4 Augmentation 11.4 Model Architecture 11.4.1 H5 Model Format 11.5 Experimental Setup 11.5.1 Digital Hair Removal (DHR) Algorithm Analysis 11.5.2 DHR-MNet Model Analysis 11.6 Comparative Study 11.7 Conclusion References Chapter 12 A Deep Hybrid System for Effective Diagnosis of Breast Cancer 12.1 Introduction 12.1.1 Contributions 12.2 Proposed Method 12.2.1 Feature Extraction 12.2.1.1 Channel Shuffle for Group Convolution 12.2.1.2 ShuffleNet Unit 12.2.1.3 Architecture 12.2.2 Classification 12.3 Datasets 12.4 Results and Discussion 12.5 Conclusion References Chapter 13 Identification of Brain Cancer Using Medical Hyperspectral Image Analysis 13.1 Introduction 13.2 Basics of Brain Cancer and Imaging Techniques 13.2.1 Brain Cancer 13.2.2 Conventional Imaging Techniques to Identify Cancerous Regions 13.3 Hyperspectral Imaging: Fundamentals and Principles 13.3.1 Basic of Hyperspectral Imaging 13.3.2 Principles of Hyperspectral Imaging Techniques 13.3.3 Medical Hyperspectral Imaging Systems 13.4 Materials and Methods 13.4.1 Acquisition of Medical Hyperspectral Images 13.4.2 In Vivo HS Brain Image Database 13.4.3 Classification Framework 13.4.4 Machine Learning for Cancer Identification 13.4.5 Previous Approaches for Cancer Detection 13.5 Challenges 13.6 Limitations 13.7 Conclusions References Chapter 14 An Efficient Deep CNN-Based AML Detection: Overcoming Small Database Limitations in Medical Applications 14.1 Introduction 14.1.1 Contribution 14.2 Proposed Method 14.2.1 Pre-processing 14.2.2 CNN Architecture 14.3 Results and Discussion 14.3.1 Datasets 14.3.2 Performance Measures 14.3.3 Performance Analysis 14.4 Conclusion Reference Chapter 15 Effective Use of Computational Biology and Artificial Intelligence in the Domain of Medical Oncology 15.1 Introduction 15.2 Computational Biology 15.2.1 Computational Biology in Oncology 15.2.2 Applications of Computational Biology in Oncology 15.2.3 Computational Models for Disease Progression and Prognosis 15.3 Artificial Intelligence 15.3.1 Artificial Intelligence in Medical Oncology 15.3.2 AI in Detecting Abnormalities and Early Disease Detection 15.3.3 AI in Drug Discovery and Development 15.4 Integration of Computational Biology and AI in Oncology 15.4.1 The Convergence of Computational Biology and AI 15.4.2 Combined Applications in Medical Research 15.4.3 Case Studies of Integrated Applications 15.5 Challenges and Future Potential 15.6 Conclusions References Chapter 16 Computer-Aided Ensemble Method for Early Diagnosis of Coronary Artery Disease 16.1 Introduction 16.2 Cardio-Oncology 16.3 Related Work 16.4 Method and Simulation 16.5 Results and Discussions 16.6 Conclusions and Future Directions References Index