Computational Intelligence for Oncology and Neurological Disorders : Current Practices and Future Directions.

Author
Panda, Mrutyunjaya [Browse]
Format
Book
Language
English
Εdition
1st ed.
Published/​Created
  • Milton : Taylor & Francis Group, 2024.
  • ©2025.
Description
1 online resource (292 pages)

Details

Series
Chapman and Hall/CRC Computational Biology Series [More in this series]
Summary note
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.
Source of description
Description based on publisher supplied metadata and other sources.
Contents
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
ISBN
  • 1-04-008562-8
  • 1-003-45015-6
Statement on language in description
Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage. Read more...
Other views
Staff view

Supplementary Information