TY - JOUR ID - 88132 TI - Breast Cancer Detection based on 3-D Mammography Images using Deep Learning Strategies JO - Journal of Information Technology Management JA - JITM LA - en SN - AU - Sagayam, K. Martin AU - Anton Jone, A. Amir AU - Cengiz, Korhan AU - Rajesh, L. AU - Elngar, Ahmed A. AD - Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore - 641114, India AD - College of Information Technology, University of Fujaiah, UAE. AD - Department of Electronics Engineering, Madras Institute of Technology, Anna University, Chennai. AD - Faculty of Computer & Artificial Intelligence, Beni-Suef University, Beni-Suef City, 62511, Egypt; College of Computer Information Technology, American University in the Emirates, United Arab Emirates. Y1 - 2022 PY - 2022 VL - 14 IS - 4 SP - 2 EP - 18 KW - Breast Cancer KW - Mammography KW - Radiologists KW - CAD KW - Deep learning KW - Convolutional Neural Network KW - Medical imaging DO - 10.22059/jitm.2022.88132 N2 - In recent scenario, women are suffering from breast cancer disease across the world. Mammography is one of the important methods to detect breast cancer early; that to reduce the cost and workload of radiologists. Medical image processing is a tremendous technique used to determine the disease in advance to reduce the risk factor. To predict the disease from 2-D mammography images for diagnosing and detecting based on advanced soft computing paradigm. Still, to get more accuracy in all coordinate axes, 3-D mammography imaging is used to capture depth information from all different angles. After the reconstruction of this process, a better quality of 3D mammography is obtained. It is useful for the experts to identify the disease in well advance. To improve the accuracy of disease findings, deep convolution neural networks (CNN) can be applied for automatic feature learning, and classifier building. This work also presents a comparison of the other state of art methods used in the last decades. UR - https://jitm.ut.ac.ir/article_88132.html L1 - https://jitm.ut.ac.ir/article_88132_b5fa3520da1d317b567f63050316231e.pdf ER -