Deep Learning Applications in Healthcare and Medical Imaging Practice | 32.61 MB
Title: Deep Learning Applications in Healthcare and Medical Imaging Practice
Author: Prabhishek Singh
Category: Nonfiction, Science & Nature, Technology, Automation, Computers, Networking & Communications, Engineering
Language: English | 487 Pages | ISBN: 9819918383
Description:
This book offers a comprehensive study of medical image processing, with an emphasis on methods to enhance the accuracy and readability of medical images for diagnosis and therapy. Its main goals are to improve the visual clarity of images by sharpening details, lowering noise, and altering contrast to better visualize subtle elements. Also, it guarantees that anatomical features are accurately represented by correcting distortions, artefacts, and defects resulting from capture or transmission. Reconstruction is the main topic, including how to mix data from different modalities (such as CT and MRI) to build a more complete perspective or recreate missing or incomplete data from fragmented images. This book covers a wide range of topics, including deep learning methodologies, image processing methods, clinical applications, evaluation, and validation. The goal of the book is to provide readers a thorough understanding of medical image processing methods for enhancing, restoring, and reconstructing medical images. It describes the different algorithms’ theoretical foundations and real-world applications. It highlights the practical uses and promise of these methods for enhancing medical diagnosis and care.
This book offers a comprehensive study of medical image processing, with an emphasis on methods to enhance the accuracy and readability of medical images for diagnosis and therapy. Its main goals are to improve the visual clarity of images by sharpening details, lowering noise, and altering contrast to better visualize subtle elements. Also, it guarantees that anatomical features are accurately represented by correcting distortions, artefacts, and defects resulting from capture or transmission. Reconstruction is the main topic, including how to mix data from different modalities (such as CT and MRI) to build a more complete perspective or recreate missing or incomplete data from fragmented images. This book covers a wide range of topics, including deep learning methodologies, image processing methods, clinical applications, evaluation, and validation. The goal of the book is to provide readers a thorough understanding of medical image processing methods for enhancing, restoring, and reconstructing medical images. It describes the different algorithms’ theoretical foundations and real-world applications. It highlights the practical uses and promise of these methods for enhancing medical diagnosis and care.
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