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Authors
Affiliations
1 University of Information Technology and Communications, Baghdad, IQ
Source
Medico-Legal Update, Vol 20, No 1 (2020), Pagination: 984-990
Abstract
Deep learning of multilayered computational models allowed processing to recognize the representation of data at multiple levels of abstraction. These technologies have significantly improved to identify breast cancer. Convolutional Neural Network (CNN) is a type of radiological basis for classification problems and is based on the Bayes decision-making base, which reduces the expected error in classification. In this paper,it is proposed to detect breast cancer using CNN of the mammography system to classify the mammogram to noncancerous abnormality and cancerous abnormality. The goal of Breast Cancer Detection based on CNN for speeding up diagnosis by helping a particular diagnose and classify breast cancer.A set of images of a mammogram is used to perform the pre-processing of the histogram equalization and adjust the appropriate parameters for the CNN work. Then, the whole changed images are set in CNN to classify as a training source. A CNN classifies can be produced as a model foridentifying the mammogram. the BCDCNN method with the mammography classification utilizing MCCANN comparison with BCDCNN improved the classificationaccuracy on mammographic. Therefore, the results showed that the proposed system has a higher resolution than the other recently existing systems, and the only mass containing all was maximized from 0.84 to 0.88 and 0.70 to 0.82 accuracy.
Keywords
Breast Cancer, Convolutional Neural Network, Artificial Neural Network, Mammography Classification, Histogram Equalization
