1st Edition

Machine Learning and Deep Learning Techniques for Medical Science

412 Pages 195 B/W Illustrations
by CRC Press

412 Pages 195 B/W Illustrations
by CRC Press

412 Pages 195 B/W Illustrations
by CRC Press

The application of machine learning is growing exponentially into every branch of business and science, including medical science. This book presents the integration of machine learning (ML) and deep learning (DL) algorithms that can be applied in the healthcare sector to reduce the time required by doctors, radiologists, and other medical professionals for analyzing, predicting, and diagnosing... Read more
 

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

S. P. Balamurugan

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

V. V. Teresa, J. Dhanasekar, V. Gurunathan, and T. Sathiyapriya

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

Aayesha Hakim and R. N. Awale

Chapter 4. Neural Networks for Medical Image Computing

V. A. Pravina, P. K. Poonguzhali, and A. Kishore Kumar

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

S. Kannadhasan and R. Nagarajan

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

P M Siva Raja, R P Sumithra, and K Ramanan

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

Madhusudan G. Lanjewar, Rajesh K. Parate, Rupesh D. Wakodikar, and Anil J. Thusoo

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

Ritu Aggarwal

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

Amen Bidani, Mohamed Salah Gouider, and Carlos M Travieso-Gonzalez

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

Giang Son Tran, Quoc Viet Kieu, and Thi Phuong Nghiem

Chapter 11. Application of Artificial Intelligence in Medical Imaging

Sampurna Panda and Rakesh Kumar Dhaka

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

M. Jayasanthi and R. Kalaivani

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

C. Thirumarai Selvi, J. Amudha, and R. Sudhakar

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

Meenakshi M. Pawer, Suvarna D. Pujari, Swati P. Pawar, and Sanjay N. Talbar

Chapter 15. Artificial Intelligence is Revolutionizing Cancer Research

B. Sudha, K. Suganya, K. Swathi, and S. Sumathi

Chapter 16. Deep Learning to Diagnose Diseases and Security in 5G Healthcare Informatics

Partha Ghosh

Chapter 17. New Approaches in Machine-based Image Analysis for Medical Oncology

E. Francy Irudaya Rani, T. LurthuPushparaj, E. Fantin Irudaya Raj, and M. Appadurai

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

K. Deepti

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

Sanat Kumar Sahu and Pratibha Verma

Biography

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.