This book offers detailed information on biomedical imaging using Deep Convolutional Neural Networks (Deep CNN). It focuses on different types of biomedical images to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, and image processing perspectives.
This book offers detailed information on biomedical imaging using Deep Convolutional Neural Networks (Deep CNN). It focuses on different types of biomedical images to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, and image processing perspectives.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Ngangbam Herojit Singh is presently working as an Assistant Professor at NIT Agartala in the Department of Computer Science and Engineering. He received his Ph.D. degree from the NIT Manipur, and M.Tech and B.Tech from Anna University, Chennai. His area of interest includes machine learning, biomedical image processing, hybrid intelligent system, and mobile robotics. He has published more than 12 referred journals, including SCI and Scopus-indexed journals. Furthermore, he has published many conference papers and book chapters. Dr. Herojit has participated in many international conferences as an organizer and session chair. Utku Kose received a B.S. degree in 2008 in computer education from Gazi University, Turkey, as a faculty valedictorian; an M.S. degree in 2010 from Afyon Kocatepe University, Turkey, in the field of computer; and a D.S. / Ph. D. degree in 2017 from Selcuk University, Turkey, in the field of computer engineering. Between 2009 and 2011, he worked as a research assistant at Afyon Kocatepe University. Furthermore, he has also worked as a lecturer and vocational school vice director at Afyon Kocatepe University between 2011 and 2012, as a lecturer and research center director at Usak University between 2012 and 2017, and as an assistant professor at Suleyman Demirel University between 2017 and 2019. Currently, he is an Associate Professor at Suleyman Demirel University, Turkey. He has to his credit more than 200 publications, including articles, authored and edited books, proceedings, and reports. He is also on the editorial boards of many scientific journals and serves as one of the editors of the Biomedical and Robotics Healthcare (CRC Press) book series. His research interest includes artificial intelligence, machine ethics, artificial intelligence safety, biomedical applications, optimization, chaos theory, distance education, e-learning, computer education, and computer science. Sarada Prasad Gochhayat is currently working as an assistant teaching professor at Villanova University, Philadelphia, USA. He has earned his Ph.D. degree in communication engineering from IISc Bangalore, M. Tech. in Signal Processing from IIT Guwahati, and B. Tech. in ECE from ITER, SOA University, Bhubaneswar. He has past working experience as a faculty member at Manipal University, India, and Old Dominion University, Virginia, USA; post-doctoral research experience at the University of Padua, Italy; and Virginia Modeling, Analysis and Simulation Center, Virginia, USA. His research interests include blockchain, privacy in cloud computing, privacy-preserving data analytics, security and privacy in healthcare systems, and security in IoT and 5G networks.
Inhaltsangabe
1. Detection of Diabetic Retinopathy from retinal fundus images by using CNN Model ResNet-50. 2. DNASNet-RF: Automated Deep NAS-network with Random Forest for Classifying and Detecting Multi-class Brain Tumor. 3. Deep CNNs in image-guided diagnosis of breast and skin cancers. 4. Robust Learning Principle Design to Detect Diabetic Retinopathy Disease in Early Stages with Skilled Feature Extraction Policy. 5. Liver Tumour Detection using Machine Learning Techniques: A Systematic Review. 6. Deep Learning in Photoacoustic Tomographic Image Reconstruction. 7. Design and Development of Computer Aided Diagnosis to Detect Lung Cancer Disease by Using Intelligent Deep Learning Principle. 8. Novel Methodology to Predict and Classify Liver Diseases Based on Hybrid Deep Learning Strategy. 9. Improvements in Analysing Biomedical Signals and Medical Images Using Deep Learning. 10. A Survey on Lung Cancer Diagnosis Using Deep Learning Techniques. 11. Content-Based Medical Image Retrieval using CNN Feature Extraction and Hashing Dimensionality Reduction. 12. Experimental Evaluation of Deep Learning-Assisted Brain Tumor Identification with Advanced Classification Methodology. 13. Study of Biomedical Segmentation Based On Recent Techniques and Deep Learning. 14. Deep CNN in Healthcare. 15. An Improved Multi-Class Breast Cancer Classification and Abnormality Detection Based On Modified Deep Learning Neural Network Principles
1. Detection of Diabetic Retinopathy from retinal fundus images by using CNN Model ResNet-50. 2. DNASNet-RF: Automated Deep NAS-network with Random Forest for Classifying and Detecting Multi-class Brain Tumor. 3. Deep CNNs in image-guided diagnosis of breast and skin cancers. 4. Robust Learning Principle Design to Detect Diabetic Retinopathy Disease in Early Stages with Skilled Feature Extraction Policy. 5. Liver Tumour Detection using Machine Learning Techniques: A Systematic Review. 6. Deep Learning in Photoacoustic Tomographic Image Reconstruction. 7. Design and Development of Computer Aided Diagnosis to Detect Lung Cancer Disease by Using Intelligent Deep Learning Principle. 8. Novel Methodology to Predict and Classify Liver Diseases Based on Hybrid Deep Learning Strategy. 9. Improvements in Analysing Biomedical Signals and Medical Images Using Deep Learning. 10. A Survey on Lung Cancer Diagnosis Using Deep Learning Techniques. 11. Content-Based Medical Image Retrieval using CNN Feature Extraction and Hashing Dimensionality Reduction. 12. Experimental Evaluation of Deep Learning-Assisted Brain Tumor Identification with Advanced Classification Methodology. 13. Study of Biomedical Segmentation Based On Recent Techniques and Deep Learning. 14. Deep CNN in Healthcare. 15. An Improved Multi-Class Breast Cancer Classification and Abnormality Detection Based On Modified Deep Learning Neural Network Principles
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