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.
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Table of Contents
1. Introduction to Deep Learning and Diagnosis in Medicine2. 1D CNN based identification of Sleep disorders using EEG signals
3. Classification of Histopathological Colon Cancer Images Using PSO based Feature Selection Algorithm
4. Arrhythmia Diagnosis from ECG Signal Pulses with One?Dimensional Convolutional Neural Network
5. Patch-based Approaches to Whole Slide Histologic Grading of Breast Cancer using Convolutional Neural Networks
6. Deep neural architecture for the breast cancer detection from medical CT image modalities
7. Automated Analysis of Phase-Contrast Optical Microscopy Time-Lapse Images: Application to Wound Healing and Cell Motility Assays of Breast Cancer
8. Automatic detection of normal structures and pathological changes in radiological chest images using deep learning methods
9. Adversarial attacks: dependence on medical image type, CNN architecture as well as on the attack and defense methods
10. A Deep Ensemble Network for Lung Segmentation with Stochastic Weighted Averaging
11. Ensemble of segmentation approaches based on convolutional neural networks
12. Classification of diseases from CT images using LSTM based CNN This chapter explains LSTM modules, CT dataset, and CT related diseases
13. A Novel Polyp Segmentation Approach using U-net with Saliency-like Feature Fusion
