Main description:

Presents key aspects in the development and the implementation of machine learning and deep learning approaches towards developing prediction tools, models, and improving medical diagnosis
Discusses recent trends innovations, challenges, solutions, and applications of intelligent system-based disease diagnosis
Examines deep learning theories, models, and tools to enhance health information systems
Explores ML and DL in relation to AI prediction tools discovery of drugs, neuroscience, and diagnosis in multiple imaging modalities

Contents:

Chapter 1. A Comprehensive Study on MLP and CNN, and the Implementation of Multi-Class Image Classification using Deep CNN

Chapter 2. An Efficient Technique for Image Compression and Quality Retrieval in Diagnosis of Brain Tumour Hyper Spectral Image

Chapter 3. Classification of Breast Thermograms using a Multi-layer Perceptron with Back Propagation Learning

Chapter 4. Neural Networks for Medical Image Computing

Chapter 5. Recent Trends in Bio-Medical Waste, Challenges and Opportunities

Chapter 6. Teager-Kaiser Boost Clustered Segmentation of Retinal Fundus Images for Glaucoma Detection

Chapter 7. IoT-Based Deep Neural Network Approach for Heart Rate and SpO2 Prediction

Chapter 8. An Intelligent System for Diagnosis and Prediction of Breast Cancer Malignant Features using Machine Learning Algorithms

Chapter 9. Medical Image Classification with Artificial and Deep Convolutional Neural Networks: A Comparative Study

Chapter 10. Convolutional Neural Network for Classification of Skin Cancer Images

Chapter 11. Application of Artificial Intelligence in Medical Imaging

Chapter 12. Machine Learning Algorithms Used in Medical Field with a Case Study

Chapter 13. Dual Customized U-Net-based Based Automated Diagnosis of Glaucoma

Chapter 14. MuSCF-Net: Multi-scale, Multi-Channel Feature Network using Resnet-Based Attention Mechanism for Breast Histopathological Image Classification

Chapter 15. Artificial Intelligence is Revolutionizing Cancer Research

Chapter 16. Deep Learning to Diagnose Diseases and Security in 5G Healthcare InformaticsChapter 17. New Approaches in Machine-based Image Analysis for Medical Oncology

Chapter 18. Performance Analysis of Deep Convolutional Neural Networks for Diagnosing COVID-19: Data to Deployment

Chapter 19. Stacked Auto Encoder Deep Neural Network with Principal Components Analysis for Identification of Chronic Kidney Disease