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Deep Learning Models for Medical Imaging (Primers in Biomedical Imaging Devices and Systems) - Tapa blanda

Libro 3 de 6: Primers in Biomedical Imaging Devices and Systems

Santosh, K. C.; Das, Nibaran; Ghosh, Swarnendu

 
9780128235041: Deep Learning Models for Medical Imaging (Primers in Biomedical Imaging Devices and Systems)
Tapa blanda
ISBN 10: 0128235047 ISBN 13: 9780128235041
Editorial: Academic Press, 2021

Sinopsis

Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow 'with' and 'without' transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientists to industrialists.

    • Provides a step-by-step approach to develop deep learning models

    • Presents case studies showing end-to-end implementation (source codes: available upon request)

"Sinopsis" puede pertenecer a otra edición de este libro.

Acerca de los autores

Prof. KC Santosh is the Chair of the Department of Computer Science at the University of South Dakota (USD). Before joining USD, Prof. Santoshworked as a research fellow at the U.S. National Library of Medicine (NLM), National Institutes of Health (NIH). He was a postdoctoral research scientist at the LORIA research centre (with industrial partner, ITESOFT (France)). He has demonstrated expertise in artificial intelligence, machine learning, pattern recognition, computer vision, image processing and data mining with applications, such as medical imaging informatics, document imaging, biometrics, forensics, and speech analysis. His research projects are funded by
multiple agencies, such as SDCRGP, Department of Education, National Science Foundation, and Asian Office of Aerospace Research and Development. He is the proud recipient of the Cutler Award for Teaching and Research Excellence (USD, 2021), the President’s Research Excellence Award (USD, 2019), and the Ignite Award from the U.S. Department

Nibaran Das received his B.Tech degree in Computer Science and Technology from Kalyani Govt. Engineering College under KalyaniUniversity, in 2003. He received his M.C.S.E. degree from Jadavpur University, in 2005. He received his Ph.D. (Engg.) degree thereafter from Jadavpur University, in 2012. He joined J.U. as a lecturer in 2006. His areas of current research interest are OCR
of handwritten text, optimization techniques, image processing, and deep learning. He has been an editor of Bengali monthly magazine Computer Jagat since 2005.

Swarnendu Ghosh is an Assistant Professor at Adamas University in the department of Computer Science and Engineering. He received his B.Tech degree in Computer Science and Engineering from West Bengal University of Technology, in 2012. He received his Masters in Computer Science and Engineering from Jadavpur University, in 2014. He has been a doctoral fellow under the Erasmus Mundus Mobility with Asia at University of Evora, Portugal. Currently he is continuing his Ph.D. on Computer Science and Engineering at Jadavpur University. His area of interest is deep learning, graph based learning, and knowledge representation.

De la contraportada

Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow ‘with’ and ‘without’ transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientists to industrialists.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Academic Press
Año de publicación
2021
Idioma
Inglés
ISBN 10 
0128235047
ISBN 13 
9780128235041
Encuadernación
Tapa blanda
Número de páginas
172
Contacto del fabricante
Academic Press
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