Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods

Häftad, Engelska, 2023

Del i serien Intelligent Data-Centric Systems

1 452 kr

Beställningsvara. Skickas inom 7-10 vardagar. Fri frakt över 249 kr.

Beskrivning

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

Produktinformation

Utgivningsdatum:2023-05-05
Mått:191 x 235 x 18 mm
Vikt:630 g
Format:Häftad
Språk:Engelska
Serie:Intelligent Data-Centric Systems
Antal sidor:302
Förlag:Elsevier Science
ISBN:9780323961295

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Artificiell intelligens inom Data och IT
Medicinsk bildbehandling inom Medicin

Mer om författaren

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. Şaban Öztürk is a researcher in Amasya University, in Turkey. He has over 50 scientific publications. Dr. Öztürk’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.

Innehållsförteckning

1. Introduction to Deep Learning and Diagnosis in Medicine2. 1D CNN based identification of Sleep disorders using EEG signals3. Classification of Histopathological Colon Cancer Images Using PSO based Feature Selection Algorithm4. Arrhythmia Diagnosis from ECG Signal Pulses with One?Dimensional Convolutional Neural Network5. Patch-based Approaches to Whole Slide Histologic Grading of Breast Cancer using Convolutional Neural Networks6. Deep neural architecture for the breast cancer detection from medical CT image modalities7. Automated Analysis of Phase-Contrast Optical Microscopy Time-Lapse Images: Application to Wound Healing and Cell Motility Assays of Breast Cancer8. Automatic detection of normal structures and pathological changes in radiological chest images using deep learning methods9. Adversarial attacks: dependence on medical image type, CNN architecture as well as on the attack and defense methods10. A Deep Ensemble Network for Lung Segmentation with Stochastic Weighted Averaging11. Ensemble of segmentation approaches based on convolutional neural networks12. Classification of diseases from CT images using LSTM based CNN This chapter explains LSTM modules, CT dataset, and CT related diseases13. A Novel Polyp Segmentation Approach using U-net with Saliency-like Feature Fusion