Deep Learning Models for Medical Imaging

Erscheinungsjahr 2021
ISBN: 978-0-12-823504-1
Verlag: William Andrew Publishing

Buch, Englisch, 170 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 360 g

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.

Zielgruppe

Engineers and biomedical engineers, medical imaging researchers and graduate students Clinical researchers

Autoren/Hrsg.

Santosh, KC

Das, Nibaran

Ghosh, Swarnendu

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Bildgebende Verfahren, Nuklearmedizin, Strahlentherapie Radiologie, Bildgebende Verfahren

Inhaltsverzeichnis

1. Introduction 2. Deep learning: a review 3. Deep learning models 4. Cytology image analysis 5. COVID-19: prediction, screening, and decision-making

Über Autor(innen)

Santosh, KC
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

Das, Nibaran
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.

Ghosh, Swarnendu
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.