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.
1. Chapter 1 : Introduction to deep learning and diagnosis in medicine - p. Page ix 2. Chapter 2 : One-dimensional convolutional neural network-based identification of sleep disorders using electroencephalogram signals - p. Page 28 3. Chapter 3 : Classification of histopathological colon cancer images using particle swarm optimization-based feature selection algorithm - p. Page 57 4. Chapter 4 : Arrhythmia diagnosis from ECG signal pulses with one-dimensional convolutional neural networks - p. Page 80 5. Chapter 5 : Patch-based approaches to whole slide histologic grading of breast cancer using convolutional neural networks - p. Page 99 6. Chapter 6 : Deep neural architecture for breast cancer detection from medical CT image modalities - p. Page 116 7. Chapter 7 : Automated analysis of phase-contrast optical microscopy time-lapse images: application to wound healing and cell motility assays of breast cancer - p. Page 133 8. Chapter 8 : Automatic detection of pathological changes in chest X-ray screening images using deep learning methods - p. Page 151 9. Chapter 9 : Dependence of the results of adversarial attacks on medical image modality, attack type, and defense methods - p. Page 177 10. Chapter 10 : A deep ensemble network for lung segmentation with stochastic weighted averaging - p. Page 194 11. Chapter 11 : Deep ensembles and data augmentation for semantic segmentation - p. Page 212 12. Chapter 12 : Classification of diseases from CT images using LSTM-based CNN - p. Page 232 13. Chapter 13 : A novel polyp segmentation approach using U-net with saliency-like feature fusion - p. Page 246 14. Index - p. Page 267
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.
1. Chapter 1 : Introduction to deep learning and diagnosis in medicine - p. Page ix 2. Chapter 2 : One-dimensional convolutional neural network-based identification of sleep disorders using electroencephalogram signals - p. Page 28 3. Chapter 3 : Classification of histopathological colon cancer images using particle swarm optimization-based feature selection algorithm - p. Page 57 4. Chapter 4 : Arrhythmia diagnosis from ECG signal pulses with one-dimensional convolutional neural networks - p. Page 80 5. Chapter 5 : Patch-based approaches to whole slide histologic grading of breast cancer using convolutional neural networks - p. Page 99 6. Chapter 6 : Deep neural architecture for breast cancer detection from medical CT image modalities - p. Page 116 7. Chapter 7 : Automated analysis of phase-contrast optical microscopy time-lapse images: application to wound healing and cell motility assays of breast cancer - p. Page 133 8. Chapter 8 : Automatic detection of pathological changes in chest X-ray screening images using deep learning methods - p. Page 151 9. Chapter 9 : Dependence of the results of adversarial attacks on medical image modality, attack type, and defense methods - p. Page 177 10. Chapter 10 : A deep ensemble network for lung segmentation with stochastic weighted averaging - p. Page 194 11. Chapter 11 : Deep ensembles and data augmentation for semantic segmentation - p. Page 212 12. Chapter 12 : Classification of diseases from CT images using LSTM-based CNN - p. Page 232 13. Chapter 13 : A novel polyp segmentation approach using U-net with saliency-like feature fusion - p. Page 246 14. Index - p. Page 267
