Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods

Kemal Polat, Saban Öztürk

• Publisher: Academic Press
• Year: 2023
• Print ISBN: 9780323961295
• eBook ISBN: 9780323996815
• Topic: Computer Science
• Subtopic: Business Intelligence
• Index: Computer Science

Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods presents comprehensive research on both medical imaging and medical signals analysis. The book 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. These image and signal modalities include real challenges that are the main themes that medical imaging and medical signal processing researchers focus on today. The book also emphasizes removing noise and specifying dataset key properties, with each chapter containing details of one of the medical imaging or medical signal modalities. Focusing on solving real medical problems using new deep learning and CNN approaches, this book will appeal to research scholars, graduate students, faculty members, R&D engineers, and biomedical engineers who want to learn how medical signals and images play an important role in the early diagnosis and treatment of diseases. - Investigates novel concepts of deep learning for acquisition of non-invasive biomedical image and signal modalities for different disorders - Explores the implementation of novel deep learning and CNN methodologies and their impact studies that have been tested on different medical case studies - Presents end-to-end CNN architectures for automatic detection of situations where early diagnosis is important - Includes novel methodologies, datasets, design and simulation examples

Table of contents

• Cover
• Title page
• Table of Contents
• Copyright
• Contents
• List of contributors
• List of Illustrations
• List of Tables
• Chapter 1 : Introduction to deep learning and diagnosis in medicine
• Chapter 2 : One-dimensional convolutional neural network-based identification of sleep disorders using electroencephalogram signals
• Chapter 3 : Classification of histopathological colon cancer images using particle swarm optimization-based feature selection algorithm
• Chapter 4 : Arrhythmia diagnosis from ECG signal pulses with one-dimensional convolutional neural networks
• Chapter 5 : Patch-based approaches to whole slide histologic grading of breast cancer using convolutional neural networks
• Chapter 6 : Deep neural architecture for breast cancer detection from medical CT image modalities
• Chapter 7 : Automated analysis of phase-contrast optical microscopy time-lapse images: application to wound healing and cell motility assays of breast cancer
• Chapter 8 : Automatic detection of pathological changes in chest X-ray screening images using deep learning methods
• Chapter 9 : Dependence of the results of adversarial attacks on medical image modality, attack type, and defense methods
• Chapter 10 : A deep ensemble network for lung segmentation with stochastic weighted averaging
• Chapter 11 : Deep ensembles and data augmentation for semantic segmentation
• Chapter 12 : Classification of diseases from CT images using LSTM-based CNN
• Chapter 13 : A novel polyp segmentation approach using U-net with saliency-like feature fusion
• Index
• A