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020 _a9781000370492
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024 7 _a10.1201/9781003144694
_2doi
037 _a9781000370492
_bIngram Content Group
037 _a9781003144694
_bTaylor & Francis
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055 0 _aRC78.7.D53
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245 0 0 _aHandbook of deep learning in biomedical engineering and health informatics /
_cedited by E. Golden Julie, PhD, Y. Harold Robinson, PhD, S. M. Jaisakthi, PhD.
246 3 _aDeep learning in biomedical engineering and health informatics
250 _aFirst edition.
264 1 _aPalm Bay, FL :
_bApple Academic Press Inc. ;
_aBoca Raton, FL :
_bCRC Press,
_c2022.
264 4 _c©2022
300 _a1 online resource (xxv, 318 pages) :
_billustrations.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
341 0 _atextual
_2sapdv
_3EBSCOhost
490 1 _aBiomedical engineering: techniques and applications
504 _aIncludes bibliographical references and index.
505 0 _aReview 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."--
_cProvided by publisher.
532 0 _3EBSCOhost
_a"EBSCO evaluates our products based on the Web Content Accessibility Guidelines (WCAG) and the related Section 508 and EN 301 549 regulations in the US and EU. Most EBSCO products are substantially conformant with WCAG 2.2 level AA." Source: https://connect.ebsco.com/s/article/EBSCO-VPATs?language=en_US. Last accessed April 22, 2025.
545 0 _aE. Golden Julie, PhD, is a Senior Assistant Professor in the Department of Computer Science and Engineering at Anna University, Regional Campus, Tirunelveli, India. With more than 12 years of experience in teaching, shehas published and presented many papers at national and international conferences. She has written ten book chapters and isco-editor ofthe books Successful Implementation and Deployment of IoT Projects in Smart Cities and Handbook of Research on Blockchain Technology: Trend and Technologies. She also acts as a reviewer for many journals on computers and electrical engineering. Y. Harold Robinson, PhD, is currently working at the School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India. He has more than 15 years of experience in teaching, has published many papers in international journals and presentedat both national and international conferences. Along with Dr. Julie, Dr. Robinson is co-editor of the books Successful Implementation and Deployment of IoT Projects in Smart Cities and Handbook of Research on Blockchain Technology: Trend and Technologies. He is a reviewer of many journals.S. M. Jaisakthi, PhD, is an Associate Professor at the School of Computer Science and Engineering at the Vellore Institute of Technology, Vellore,India. Dr. Jaisakthi has extensive research experience in machine learning in image processing, medical image analysis and in building deep learning models. She has published many research publications in refereed international journals and in proceedings of international conferences. Currently she is investigating a project funded by the Science and Engineering Research Board (SERB).
588 _aDescription based on online resource; title from digital title page (viewed on July 19, 2023).
650 0 _aArtificial intelligence.
_958441
650 0 _aMedical informatics.
_960693
650 0 _aBiomedical engineering.
_963886
650 0 _aDeep learning (Machine learning)
_963887
650 1 2 _aMedical Informatics
_960693
650 1 2 _aDeep Learning
_955053
650 2 2 _aBiomedical Engineering
_963888
650 2 _aDiagnostic Imaging
_963889
650 2 _aArtificial Intelligence
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650 7 _aartificial intelligence.
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650 7 _abiomedical engineering.
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650 7 _aMEDICAL / Biotechnology
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690 _aArtificial intelligence.
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700 1 _aJulie, Golden,
_d1984-
_eeditor.
_963892
700 1 _aJaisakthi, S. M.,
_eeditor.
_963893
700 1 _aRobinson, Y. Harold,
_eeditor.
_963894
776 0 8 _iPrint version:
_tHandbook of deep learning in biomedical engineering and health informatics.
_bFirst edition.
_dPalm Bay, FL, USA ; Burlington, ON, Canada : Apple Academic Press ; Boca Raton, FL, USA ; Abingdon, Oxon, UK : CRC Press, 2021
_z1771889985
_z9781771889988
830 0 _aBiomedical engineering (Apple Academic Press)
_963895
856 4 0 _3EBSCOhost
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