Deep Learning in Biomedical Signal and Medical Imaging

Ngangbam Herojit Singh, Utku Kose, Sarada Prasad Gochhayat

• Publisher: CRC Press
• Year: 2024
• eBook ISBN: 9781040107171
• Edition: 0
• Topic: Computer Science
• Subtopic: Computer Engineering
• Index: Computer Science

This book offers detailed information on biomedical imaging using Deep Convolutional Neural Networks (Deep CNN). It focuses on different types of biomedical images to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis and image processing perspectives.

Deep Learning in Biomedical Signal and Medical Imaging discusses classification, segmentation, detection, tracking, and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT, and X-RAY, amongst others. It surveys the most recent techniques and approaches in this field, with both broad coverage and enough depth to be of practical use to working professionals. It includes examples of the application of signal and image processing employing Deep CNN to Alzheimer's, brain tumor, skin cancer, breast cancer, and stroke prediction, as well as ECG and EEG signals. This book offers enough fundamental and technical information on these techniques, approaches, and related problems without overcrowding the reader's head. It presents the results of the latest investigations in the field of Deep CNN for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine the fundamental theory of artificial intelligence (AI), machine learning (ML,) and Deep CNN with practical applications in biology and medicine. Certainly, the list of topics covered in this book is not exhaustive, but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis.

The book is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educators who are working in the context of the topics.

Table of contents

• Cover
• Half Title
• Series
• Title
• Copyright
• Contents
• About the Editors
• List of Contributors
• Chapter 1 Detection of Diabetic Retinopathy from Retinal Fundus Images by Using CNN Model ResNet-50
• Chapter 2 DNASNet-RF: Automated Deep NAS-Network with Random Forest for Classifying and Detecting Multi-Class Brain Tumor
• Chapter 3 Deep CNNs in Image-Guided Diagnosis of Breast and Skin Cancers
• Chapter 4 Robust Learning Principle Design to Detect Diabetic Retinopathy Disease in Early Stages with Skilled Feature Extraction Policy
• Chapter 5 Liver Tumour Detection Using Machine Learning Techniques: A Systematic Review
• Chapter 6 Deep Learning in Photoacoustic Tomographic Image Reconstruction
• Chapter 7 Design and Development of Computer-Aided Diagnosis to Detect Lung Cancer Disease by Using Intelligent Deep Learning Principle
• Chapter 8 Novel Methodology to Predict and Classify Liver Diseases Based on Hybrid Deep Learning Strategy
• Chapter 9 Improvements in Analyzing Biomedical Signals and Medical Images Using Deep Learning
• Chapter 10 A Survey on Lung Cancer Diagnosis Using Deep Learning Techniques
• Chapter 11 Content-Based Medical Image Retrieval Using CNN Feature Extraction and Hashing
• Chapter 12 Experimental Evaluation of Deep Learning-Assisted Brain Tumor Identification with Advanced Classification Methodology
• Chapter 13 Study of Biomedical Segmentation Based on Recent Techniques and Deep Learning
• Chapter 14 Deep CNN in Healthcare
• Chapter 15 An Improved Multi-Class Breast Cancer Classification and Abnormality Detection Based on Modified Deep Learning Neural Network Principles
• Index