Diagnostic Biomedical Signal and Image Processing Applications: With Deep Learning Methods

Analysis of medical signals and images plays an important role in the early diagnosis and treatment of diseases. Thanks to the development of technology, widespread use of medical imaging devices has become beneficial to human health. However, despite advances in technology, the number of patients and the workload of healthcare professionals is ever increasing. Deep learning methods and approaches have the potential to help relieve the workload of doctors and facilitate early diagnosis. This can help to improve the healthcare system and people's quality of life.Diagnostic Biomedical Signal and Image Processing Applications presents comprehensive research focusing on both medical imaging and medical signals analysis. It discusses classification, segmentation, detection, tracking, and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT, and X-RAY, among others. These image and signal modalities include real challenges, which are the main themes that medical imaging and medical signal processing researchers focus on today. The proposed 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. Investigates novel concepts of deep learning for acquisition of non-invasive biomedical image and signal modalities for different disorders Explores implementation of novel deep learning and CNN methodologies and their impact studies 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 INDICE: 1. Introduction to Deep Learning and Diagnosis in Medicine 2. Skin lesion segmentation using novel CNN approach 3. 1D CNN based identification of Sleep disorders using EEG signals 4. Emotion recognition using hybrid DL method 5. X-RAY specific 2D CNN model 6. Classification of diseases from CT images using LSTM based CNN 7. Tracking and detection mitosis from microscopy videos 8. Whole-slide histopathological image classification using patch-based CNN 9. CMotor-Imagery Tasks Classification in BCI 10. CMachine Learning techniques for the development of smart health 11. Volumetric medical image segmentation using 3D CNN 12. Classification with unbalanced medical datasets 13. A novel DL approach for ECG signal delineation 14. EMG Signal Variations in Fatiguing Contractions of Muscles

• ISBN: 978-0-323-96129-5
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 300
• Fecha Publicación: 01/05/2023
• Nº Volúmenes: 1
• Idioma: Inglés