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Sinopsis
Application of Artificial Intelligence in Early Detection of Lung Cancer presents the most up-to-date computer-aided diagnosis techniques used to effectively predict and diagnose lung cancer. The presence of pulmonary nodules on lung parenchyma is often considered an early sign of lung cancer, thus using machine and deep learning technologies to identify them is key to improve patients’ outcome and decrease the lethal rate of such disease. The book discusses topics such as basics of lung cancer imaging, pattern recognition techniques, deep learning, and nodule detection and localization. In addition, the book discusses risk prediction based on radiological analysis and 3D modeling. This is a valuable resource for cancer researchers, oncologists, graduate students, radiologists, and members of biomedical field who are interested in the potential of AI technologies in the diagnosis of lung cancer.
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Dr. Madhuchanda Kar received her MD (Internal Medicine) and PhD (Cancer Research) degrees from University of Calcutta. She has almost 30 years of experience in medical education and research. In her career, nearly 80 articles have been published in different peer-reviewed medical and scientific journals. She is the recipient of various fellowship, e.g., Fellow of Indian College of Physicians, IMA Institute of Medical Sciences, Indian medical association, and Indian Association of Clinical Medicine. She was the chairperson of Board of Governors of Indian Institute of Science Education and Research. Presently, she is member of Board of Governors of Central University of Gaya. Her research interests have been focused on solid tumors, hematological malignancies, and interdisciplinary research linking cancer diagnosis and technology.
Jhilam Mukherjee PhD is currently a Senior Research Fellow of Centre of Excellence in Systems Biology and Biomedical Engineering, University of Calcutta, India. She received MCA from West Bengal University of Technology. She has completed her PhD in lung cancer detection using CT images from University of Calcutta and has more than five years of experience on computer-aided diagnosis for lung cancer. Her research interest on medical image processing, computer vision, and data analytics.
Dr. Amlan Chakrabarti PhD is former Dean, Faculty of Engineering and Technology of University of Calcutta. He was a Post-Doctoral fellow at the School of Engineering, Princeton University, USA, and has almost 20 years of experience in education and research. He is the recipient of Indian National Science Academy Visiting Faculty Fellowship (2014), JSPS Invitation Research Award (2016), Erasmus Mundus Leaders Award (2017), Hamied Visiting Professorship from University of Cambridge (2018) and Siksha Ratna Award by Dept. of Higher Education Govt. of West Bengal (2018). He is Associate Editor of the Elsevier’s Journal of Computers and Electrical Engineering and holds 4 patents. His areas of research are Machine Learning, Computer Vision, Reconfigurable Computing, Cyber-physical Systems, VLSI CAD, and Quantum Computing.
Sayan Das MD is associate consultant in the Department of Interventional Radiology and Imaging, Peerless Hospital Kolkata. He has received his MD degree from Kathmandu University and has published several research papers on the characteristics of radiological imaging. His research interests are on interventional radiology and imaging and CAD methodologies using different modalities of radiological images.
De la contraportada
Application of Artificial Intelligence in Early Detection of Lung Cancer presents the most up-to-date computer-aided diagnosis techniques used to effectively predict and diagnose lung cancer. The presence of pulmonary nodules on lung parenchyma is often considered as the early sign of lung cancer, thus using machine and deep learning technologies to identify them is key to improve patients’ outcome and decrease the lethal rate of such disease. The book discusses topics such as basics of lung cancer imaging; pattern recognition techniques; deep learning; and nodule detection and localization. In addition, it discusses risk prediction based on radiological analysis and 3D modeling. It is a valuable resource for cancer researchers, oncologists, graduate students, radiologists, and members of biomedical field who are interested in the potential of AI technologies in the diagnosis of lung cancer.
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AI IN EARLY DETECTION OF LUNG CANCE
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Application of Artificial Intelligence in Early Detection of Lung Cancer
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