Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods
With Deep Learning Methods
Published on 5. May 2023
Book
Paperback/Softback
302 pages
978-0-323-96129-5 (ISBN)
Description
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.
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.
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Elsevier Science & Technology
Target group
Professional and scholarly
Graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science
Dimensions
Height: 235 mm
Width: 191 mm
Weight
630 gr
ISBN-13
978-0-323-96129-5 (9780323961295)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Kemal Polat | Saban Öztürk
Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods
E-Book
04/2023
Academic Press
€130.00
Available for download
Persons
Professor Dr. Kemal Polat is a Professor in the Electrical and Electronic Engineering Department, Engineering of Faculty, Bolu Abant Izzet Baysal University, in Turkey. He has over 130 articles published in leading scientific journals and around 80 international conference papers. He is, amongst others, a member of the editorial board of the Journal of Neural Computing and Applications and editorial board member of Applied Soft Computing, Elsevier. His current research interests are in biomedical signal classification, control systems, electronics, statistical signal processing, visual memory, neuroscience, brain-computer interface, PPG signal, medical electronics, digital signal processing, pattern recognition, and classification. Dr. Saban OEztuerk is a researcher in Amasya University, in Turkey. He has over 50 scientific publications. Dr. OEztuerk's current research interests are in the fields of biomedical image processing, histopathological image analysis, hashing, content-based image retrieval, siamese networks and loss function, metric learning, deep learning, and image representation.
Content
1. Introduction to Deep Learning and Diagnosis in Medicine
2. 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
2. 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