Full Description
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.
Contents
Chapter 1: Advancements in AI for Mental Health: Exploring ASD, ADHD and Schizophrenia, Video Datasets, and Future Directions. Chapter 2: Blockchain Applications in Neurological Disorders and Oncology. Chapter 3: Deep Scattering Wavelet Network and Marine Predators Algorithm-Based Stuttering Disfluency Detection. Chapter 4: AI in Neurological Disorders : A Systematic Review. Chapter 5: Malformation Risk Prediction with Machine Learning Modelling for Pregnant Women with Epilepsy. Chapter 6: The Computational Techniques in Mutational Disease Prediction: A Comprehensive and Comparative Review. Chapter 7: Comparative Analysis of U-Net and DeepLab for Accurate Brain MRI Segmentation. Chapter 8: A Comprehensive Review on Depression Detection Based on Text from Social Media Posts. Chapter 9: Artificial Intelligence in Radiation Oncology. Chapter 10: A Comprehensive Overview of AI Applications in Radiation Oncology. Chapter 11: Melanoma Skin Cancer Identification on Embedded Devices Using Digital Hair Removal and Transfer Learning. Chapter 12: A Deep Hybrid System for Effective Diagnosis of Breast Cancer. Chapter 13: Identification of Brain Cancer Using Medical Hyperspectral Image Analysis. Chapter 14: An Efficient Deep CNN-Based AML Detection: Overcoming Small Database Limitations in Medical Applications. Chapter 15: Effective Use of Computational Biology and Artificial Intelligence in the Domain of Medical Oncology. Chapter 16: A Computer Aided Ensemble Method for Early and Accurate Detection of Coronary Artery Disease
