Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods

Автор: literator от 6-05-2023, 18:57, Коментариев: 0


Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning MethodsНазвание: Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods
Автор: Kemal Polat, Saban Ozturk
Издательство: Academic Press/Elsevier
Год: 2023
Страниц: 303
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

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.

Deep learning (DL), which has transformed several industries over the past ten years, has astounded practitioners with its amazing performance across nearly all application domains. The following fundamental ideas related to DL can be arranged in order of breadth to depth: (1) the brain-inspired artificial neural network (ANN) algorithm; (2) Machine Learning (ML) methods that allow machines to learn from examples without having to be explicitly programmed; and (3) Artificial Intelligence (AI), a theory that aims to artificially mimic the intelligent behavior of beings found in nature.

This section is divided into the following subsets: 2.1. CNN, 2.2. RNN, 2.3. AE, 2.4. GAN, and 2.5. Other DL architectures, each of which is described below. The “Deep Learning” book, considered the standard reference work for the DL community, addresses practically all of the DL algorithms that have been studied, beginning with linear algebra and probability. In addition, review articles that explore DL algorithms and discuss the principles and taxonomy of DL in medicine have been widely used. CNN is a subclass of ANN and is one of the most extensively used DL algorithms for object recognition. CNN is a highly recommended method that provides very efficient solutions in this area and is regularly used to tackle difficulties related to image processing. The CNN algorithm, which is based on a feedforward NN, has input, convolution, pooling (or subsampling), fully connected, and output layers.

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

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