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Handbook of deep learning in biomedical engineering and health informatics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Handbook of deep learning in biomedical engineering and health informaticsedited by E. Golden Julie, PhD, Y. Harold Robinson, PhD, S.M. Jaisakthi, PhD.
other author:	Jaisakthi, S. M.
Published:	Palm Bay, FL :Apple Academic Press,2022.
Description:	1 online resource :ill.
Subject:	Artificial intelligence.
Online resource:	https://www.taylorfrancis.com/books/9781003144694
ISBN:	9781003144694$q(electronic bk.)

Handbook of deep learning in biomedical engineering and health informatics
Handbook of deep learning in biomedical engineering and health informatics[electronic resource] /edited by E. Golden Julie, PhD, Y. Harold Robinson, PhD, S.M. Jaisakthi, PhD. - 1st ed. - Palm Bay, FL :Apple Academic Press,2022. - 1 online resource :ill. - Biomedical engineering : techniques and applications. - Biomedical engineering (Apple Academic Press).

Includes bibliographical references and index.

Review of Existing Systems in Biomedical Using Deep Learning Algorithms / M. Sowmiya, C. Thilagavathi, M. Rajeswari, R. Divya, K. Anusree, and Shyam Krishna -- An Overview of Convolutional Neural Network Architecture and Its Variants in Medical Diagnostics of Cancer and COVID-19 / M. Pavithra, R. Rajmohan, T. Ananth Kumar, and S.G. Sandhya -- Technical Assessment on Various Image Stitching Techniques: A Deep Learning Approach / M. Anly Antony, R. Satheesh Kumar, G.R. Gnana King, V. Yuvaraj, and Chidambaranathan -- CCNN: A Deep Learning Approach for an Acute Neurocutaneous Syndrome via Cloud-Based MRI Images / S. Arunmozhi Selvi, T. Ananth Kumar, and R.S. Rajesh -- Critical Investigation and Prototype Study on Deep Brain Stimulations: An Application of Biomedical Engineering in Healthcare / V. Milner Paul, S.R. Boselin Prabhu, T. Jarin, and T. Ananth Kumar -- Insight into Various Algorithms for Medical Image Analyzes Using Convolutional Neural Networks (Deep Learning) / S. Sundaresan, K. Suresh Kumar, V. Kishore Kumar, and A. Jayakumar -- Exploration of Deep RNN Architectures: LSTM and GRU in Medical Diagnostics of Cardiovascular and Neuro Diseases / R. Rajmohan, M. Pavithra, T. Ananth Kumar, and P. Manjubala -- Medical Image Classification and Manifold Disease Identification Through Convolutional Neural Networks: A Research Perspective / K. Suresh Kumar, A.S. Radhamani, S. Sundaresan, and T. Ananth Kumar -- Melanoma Detection on Skin Lesion Images Using K-Means Algorithm and SVM Classifier / M. Julie Therese, A. Devi, and G. Kavya -- Role of Deep Learning Techniques in Detecting Skin Cancer: A Review / S.M. Jaisakthi and B. Devikirubha -- Deep Learning and Its Applications in Biomedical Image Processing / V.V. Satyanarayana Tallapragada.

"This new volume discusses state-of-the-art deep learning techniques and approaches that can be applied in biomedical systems and health informatics. Deep learning in the biomedical field is an effective method of collecting and analyzing data that can be used for the accurate diagnosis of disease. This volume delves into a variety of applications, techniques, algorithms, platforms, and tools used in this area, such as image segmentation, classification, registration, and computer-aided analysis. The editors proceed on the principle that accurate diagnosis of disease depends on image acquisition and interpretation. There are many methods to get high resolution radiological images, but we are still lacking in automated image interpretation. Currently deep learning techniques are providing a feasible solution for automatic diagnosis of disease with good accuracy. Analyzing clinical data using deep learning techniques enables clinicians to diagnose diseases at an early stage and treat the patients more effectively. Chapters explore such approaches as deep learning algorithms, convolutional neural networks and recurrent neural network architecture, image stitching techniques, deep RNN architectures, and more. The volume also depicts how deep learning techniques can be applied for medical diagnostics of several specific health scenarios, such as cancer, COVID-19, acute neurocutaneous syndrome, cardiovascular and neuro diseases, skin lesions and skin cancer, etc. Key features: Introduces important recent technological advancements in the field, describes the various techniques, platforms, and tools used in biomedical deep learning systems, includes informative case studies that help to explain the new technologies. Handbook of Deep Learning in Biomedical Engineering and Health Informatics provides a thorough exploration of biomedical systems applied with deep learning techniques and will provide valuable information for researchers, medical and industry practitioners, academicians, and students."--

ISBN: 9781003144694$q(electronic bk.)

LCCN: 2021008116Subjects--Topical Terms:

194058
Artificial intelligence.

LC Class. No.: RC78.7.D53 / H36 2022

Dewey Class. No.: 616.0754

National Library of Medicine Call No.: W26.5

Handbook of deep learning in biomedical engineering and health informatics
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245 0 0 $a Handbook of deep learning in biomedical engineering and health informatics $h [electronic resource] / $c edited by E. Golden Julie, PhD, Y. Harold Robinson, PhD, S.M. Jaisakthi, PhD.
250 $a 1st ed.
260 $a Palm Bay, FL : $b Apple Academic Press, $c 2022.
300 $a 1 online resource : $b ill.
490 1 $a Biomedical engineering : techniques and applications
504 $a Includes bibliographical references and index.
505 0 $a Review of Existing Systems in Biomedical Using Deep Learning Algorithms / M. Sowmiya, C. Thilagavathi, M. Rajeswari, R. Divya, K. Anusree, and Shyam Krishna -- An Overview of Convolutional Neural Network Architecture and Its Variants in Medical Diagnostics of Cancer and COVID-19 / M. Pavithra, R. Rajmohan, T. Ananth Kumar, and S.G. Sandhya -- Technical Assessment on Various Image Stitching Techniques: A Deep Learning Approach / M. Anly Antony, R. Satheesh Kumar, G.R. Gnana King, V. Yuvaraj, and Chidambaranathan -- CCNN: A Deep Learning Approach for an Acute Neurocutaneous Syndrome via Cloud-Based MRI Images / S. Arunmozhi Selvi, T. Ananth Kumar, and R.S. Rajesh -- Critical Investigation and Prototype Study on Deep Brain Stimulations: An Application of Biomedical Engineering in Healthcare / V. Milner Paul, S.R. Boselin Prabhu, T. Jarin, and T. Ananth Kumar -- Insight into Various Algorithms for Medical Image Analyzes Using Convolutional Neural Networks (Deep Learning) / S. Sundaresan, K. Suresh Kumar, V. Kishore Kumar, and A. Jayakumar -- Exploration of Deep RNN Architectures: LSTM and GRU in Medical Diagnostics of Cardiovascular and Neuro Diseases / R. Rajmohan, M. Pavithra, T. Ananth Kumar, and P. Manjubala -- Medical Image Classification and Manifold Disease Identification Through Convolutional Neural Networks: A Research Perspective / K. Suresh Kumar, A.S. Radhamani, S. Sundaresan, and T. Ananth Kumar -- Melanoma Detection on Skin Lesion Images Using K-Means Algorithm and SVM Classifier / M. Julie Therese, A. Devi, and G. Kavya -- Role of Deep Learning Techniques in Detecting Skin Cancer: A Review / S.M. Jaisakthi and B. Devikirubha -- Deep Learning and Its Applications in Biomedical Image Processing / V.V. Satyanarayana Tallapragada.
520 $a "This new volume discusses state-of-the-art deep learning techniques and approaches that can be applied in biomedical systems and health informatics. Deep learning in the biomedical field is an effective method of collecting and analyzing data that can be used for the accurate diagnosis of disease. This volume delves into a variety of applications, techniques, algorithms, platforms, and tools used in this area, such as image segmentation, classification, registration, and computer-aided analysis. The editors proceed on the principle that accurate diagnosis of disease depends on image acquisition and interpretation. There are many methods to get high resolution radiological images, but we are still lacking in automated image interpretation. Currently deep learning techniques are providing a feasible solution for automatic diagnosis of disease with good accuracy. Analyzing clinical data using deep learning techniques enables clinicians to diagnose diseases at an early stage and treat the patients more effectively. Chapters explore such approaches as deep learning algorithms, convolutional neural networks and recurrent neural network architecture, image stitching techniques, deep RNN architectures, and more. The volume also depicts how deep learning techniques can be applied for medical diagnostics of several specific health scenarios, such as cancer, COVID-19, acute neurocutaneous syndrome, cardiovascular and neuro diseases, skin lesions and skin cancer, etc. Key features: Introduces important recent technological advancements in the field, describes the various techniques, platforms, and tools used in biomedical deep learning systems, includes informative case studies that help to explain the new technologies. Handbook of Deep Learning in Biomedical Engineering and Health Informatics provides a thorough exploration of biomedical systems applied with deep learning techniques and will provide valuable information for researchers, medical and industry practitioners, academicians, and students."-- $c Provided by publisher.
530 $a Issued also in electronic format.
588 $a Description based on print version record.
650 0 $a Artificial intelligence. $3 194058
650 0 $a Biomedical engineering. $3 190330
650 0 $a Diagnostic imaging. $3 214977
650 0 $a Medical informatics. $3 196487
650 2 $a Artificial Intelligence. $3 212515
650 2 $a Diagnostic Imaging. $3 188996
650 1 2 $a Deep Learning. $3 927365
650 1 2 $a Medical Informatics. $3 252147
650 2 2 $a Biomedical Engineering. $3 190464
700 1 $a Jaisakthi, S. M. $3 973098
700 1 $a Julie, Golden, $d 1984- $3 973099
700 1 $a Robinson, Y. Harold. $3 928576
830 0 $a Biomedical engineering (Apple Academic Press) $3 948265
856 4 0 $u https://www.taylorfrancis.com/books/9781003144694