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Computational Intelligence for Oncology and Neurological Disorders : Current Practices and Future Directions / Mrutyunjaya Panda [and three others].
Author
Panda, Mrutyunjaya
[Browse]
Format
Book
Language
English
Εdition
First edition.
Published/Created
Boca Raton, Florida : CRC Press, [2025]
©2025
Description
1 online resource (292 pages)
Details
Subject(s)
Medical informatics
[Browse]
Series
Chapman and Hall/CRC focus case studies in analytics and OR.
[More in this series]
Chapman and Hall/CRC Computational Biology 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.
Description based on print version record.
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.
Show 222 more Contents items
ISBN
1-04-008565-2
1-04-008562-8
1-003-45015-6
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