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Convolutional neural networks for medical image processing applications
"With the development of technology, living standards rise and people's expectations increase. This situation makes itself felt strikingly especially in the medical field. The use of medical devices is rapidly increasing to protect human health. It is very important to quickly evaluate the images obtained from these medical imaging devices. For this purpose, artificial intelligence (AI) methods are used. While hand-crafted methods were preferred in the past, more advanced methods are preferred today. CNN architectures are one of the most effective AI methods today. This book contains applications for the use of CNN methods for medical applications. The content of the book, in which different CNN methods are applied to various medical image processing problems, is quite extensive. Readers will be able to comprehensively analyze the effects of CNN methods presented in the book on medical applications"--
Convolutional Neural Networks for Segmentation in Short-Axis Cine Cardiac Magnetic Resonance Imaging : Review and Considerations / Manuel Pérez-Pelegrí, José V. Monmeneu, María P. López-Lereu and David Moratal -- Comparison of Traditional Machine Learning Algorithms and Convolution Neural Networks for Detection of Peripheral Malarial Parasites in Blood Smears / Aravinda C.V., Meng Lin, Udaya Kumar Reddy K.R., H.N. Prakash, Amar Prabhu G. and Sudeepa K.B. -- Deep Learning-Based Computer-Aided Diagnosis System for Attention Deficit Hyperactivity Disorder Classification Using Synthetic Data / Gulay Cicek and Aydın Akan -- Basic Ensembles of Vanilla-Style Deep Learning Models Improve Liver Segmentation from CT Images / A. Emre Kavur, Ludmila I. Kuncheva and M. Alper Selver -- Convolutional Neural Networks for Medical Image Analysis / Rajesh Gogineni and Ashvini Chaturvedi -- Ulcer and Red Lesion Detection in Wireless Capsule Endoscopy Images using CNN / Said Charfi, Mohamed El Ansari, Ayoub Ellahyani and Ilyas El Jaafari -- Do More With Less : Deep Learning in Medical Imaging / Shivani Rohilla, Mahipal Jadeja and Emmanuel S. Pilli -- Automatic Classification of fMRI Signals from Behavioral, Cognitive and Affective Tasks Using Deep Learning / Cemre Candemir, Osman Tayfun Bişkin, Mustafa Alper Selver and Ali Saffet Gönül -- Detection of COVID-19 in Lung CT-Scans using Reconstructed Image Features / Ankita Sharma and Preety Singh -- Dental Image Analysis : Where Deep Learning Meets Dentistry / Bernardo Silva, Laís Pinheiro, Katia Andrade, Patrícia Cury and Luciano Oliveira -- Malarial Parasite Detection in Blood Smear Microscopic Images : A Review on Deep Learning Approaches / Kinde Anlay Fante and Fetulhak Abdurahman -- Automatic Classification of Coronary Stenos is using Convolutional Neural Networks and Simulated Annealing / Luis Diego Rendon-Aguilar, Ivan Cruz-Aceves, Arturo Alfonso, Fernandez-Jaramillo, Ernesto Moya-Albor, Jorge Brieva and Hiram Ponce -- Deep Learning Approach for Detecting COVID-19 from Chest X-ray Images / Murali Krishna Puttagunta, S. Ravi and C. Nelson Kennedy Babu.
Includes bibliographical references and index.
Saban Ozturk is an Associate Professor in Amasya University, Amasya, Turkey. He obtained his B.S., M.S. Ph.D. in Electrical and Electronics Engineering from Selcuk University, Turkey in 2011, 2015, and 2019, respectively. He lectures in artificial intelligence and image processing related courses at the Amasya University. Also, he is the head of the Visual Understanding in Biomedical Images laboratory. His research interests encompass artificial intelligence, medical image analysis and deep learning applications. He has more than 50 published articles and proceedings.