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This book presents the integration of machine learning and deep learning algorithms that can be applied in the healthcare sector to reduce the time needed by doctors, radiologists, and other medical professionals to analyze, predict, and diagnose conditions with accurate results.
List of 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
About the author
Dr. K. Gayathri Devi is a Professor at the Department of Electronics and Communication Engineering, Dr. N.G.P Institute of Technology, Tamilnadu, India.
Dr Kishore Balasubramanian is an Assistant Professor (Senior Scale) in the Department of EEE at Dr. Mahalingam College of Engineering & Technology, India.
Dr. Le Anh Ngoc is a Vice Dean of Electronics and Telecommunications Faculty, Electric Power University, Hanoi, Vietnam.
Summary
This book presents the integration of machine learning and deep learning algorithms that can be applied in the healthcare sector to reduce the time needed by doctors, radiologists, and other medical professionals to analyze, predict, and diagnose conditions with accurate results.
