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Application of deep learning methods in healthcare and medical science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Application of deep learning methods in healthcare and medical scienceedited by Rohit Tanwar ... [et al.]
other author:	Tanwar, Rohit.
Published:	Palm Bay, FL :Apple Academic Press,2023.
Description:	1 online resource.
Subject:	Medical technology.
Online resource:	https://www.taylorfrancis.com/books/9781003303855
ISBN:	9781003303855$q(electronic bk.)

Application of deep learning methods in healthcare and medical science
Application of deep learning methods in healthcare and medical science[electronic resource] /edited by Rohit Tanwar ... [et al.] - 1st ed. - Palm Bay, FL :Apple Academic Press,2023. - 1 online resource.

Includes bibliographical references and index.

"This volume provides a wealth of up-to-date information on developments and applications of deep learning in healthcare and medicine. It aims to provide deep insight and understanding of novel applications that address the tough questions of disease diagnosis, prevention, and immunization. The volume looks at applications of deep learning for major medical challenges such as cancer detection and identification, birth asphyxia among neonates, kidney abnormalities, white blood cell segmentation, diabetic retinopathy detection, and Covid-19 diagnosis, prevention, and immunization. The volume discusses applications of deep learning in detection, diagnosis, intensive examination and evaluation, genomic sequencing, convolutional neural networks for image recognition and processing, and more for health issues such as kidney problems, brain tumors, lung damage, and breast cancer. The authors look at ML for brain tumor segmentation, in lung CT scans, in digital X-Ray devices, and for a logistic and transport systems for effective delivery of healthcare. Chapters include studies and discussions on chest X-ray images using CNN to identify Covid-19 infections, lung CT scan images using pre-trained VGG-16 and 3-layer CNN to distinguish Covid and non-Covid patients, genomic sequencing to study the Covid virus, breast cancer identification using CNN, brain tumor detection using multimodal image fusion and segmentation, factors responsible for birth asphyxia in neonates, and much more. It also explores cancer identification and detection using deep learning methods in the human body through algorithms based on issues, laboratory tests, imaging tests, biopsies, bone scans, computerized tomography scans, positron emission tomography, and ultrasound. This volume, Application of Deep Learning Methods in Healthcare and Medical Science, showcases the diverse applications of patient-based data collection and analysis in medicine and healthcare using computer algorithms for effective health diagnosis, prevention, and patient care"--

ISBN: 9781003303855$q(electronic bk.)

LCCN: 2022031831Subjects--Topical Terms:

226338
Medical technology.

LC Class. No.: R855.3

Dewey Class. No.: 610.285

National Library of Medicine Call No.: W 26.55.A7

Application of deep learning methods in healthcare and medical science
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245 0 0 $a Application of deep learning methods in healthcare and medical science $h [electronic resource] / $c edited by Rohit Tanwar ... [et al.]
250 $a 1st ed.
260 $a Palm Bay, FL : $b Apple Academic Press, $c 2023.
300 $a 1 online resource.
504 $a Includes bibliographical references and index.
520 $a "This volume provides a wealth of up-to-date information on developments and applications of deep learning in healthcare and medicine. It aims to provide deep insight and understanding of novel applications that address the tough questions of disease diagnosis, prevention, and immunization. The volume looks at applications of deep learning for major medical challenges such as cancer detection and identification, birth asphyxia among neonates, kidney abnormalities, white blood cell segmentation, diabetic retinopathy detection, and Covid-19 diagnosis, prevention, and immunization. The volume discusses applications of deep learning in detection, diagnosis, intensive examination and evaluation, genomic sequencing, convolutional neural networks for image recognition and processing, and more for health issues such as kidney problems, brain tumors, lung damage, and breast cancer. The authors look at ML for brain tumor segmentation, in lung CT scans, in digital X-Ray devices, and for a logistic and transport systems for effective delivery of healthcare. Chapters include studies and discussions on chest X-ray images using CNN to identify Covid-19 infections, lung CT scan images using pre-trained VGG-16 and 3-layer CNN to distinguish Covid and non-Covid patients, genomic sequencing to study the Covid virus, breast cancer identification using CNN, brain tumor detection using multimodal image fusion and segmentation, factors responsible for birth asphyxia in neonates, and much more. It also explores cancer identification and detection using deep learning methods in the human body through algorithms based on issues, laboratory tests, imaging tests, biopsies, bone scans, computerized tomography scans, positron emission tomography, and ultrasound. This volume, Application of Deep Learning Methods in Healthcare and Medical Science, showcases the diverse applications of patient-based data collection and analysis in medicine and healthcare using computer algorithms for effective health diagnosis, prevention, and patient care"-- $c Provided by publisher.
588 $a Description based on print version record and CIP data provided by publisher; resource not viewed.
650 0 $a Medical technology. $3 226338
650 0 $a Medical care $x Technological innovations. $3 567541
650 0 $a Deep learning (Machine learning) $3 913129
650 1 2 $a Medical Informatics. $3 252147
650 1 2 $a Deep Learning. $3 927365
700 1 $a Tanwar, Rohit. $3 973085
856 4 0 $u https://www.taylorfrancis.com/books/9781003303855