文章基本信息

标题：Breast Cancer Classification Using GLCM and BPNN
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作者：Syam Julio A. Sarosa ; Fitri Utaminingrum ; Fitra A. Bachtiar 等
期刊名称：International Journal of Advances in Soft Computing and Its Applications
印刷版ISSN：2074-8523
出版年度：2019
卷号：11
期号：3
页码：157-172
出版社：International Center for Scientific Research and Studies
摘要：Among all the cancer in women, breast cancer is the most common and deadliest cancer. In 2018, there were 22.692 Indonesian women dead because of breast cancer. Until now, the main cause of breast cancer is still unknown. However, the possibility of recovery and survival rates can be increased through early detection. One of the most efficient ways of early detection is through mammography. Mammography produces images called mammograms. The main objective of this paper is to develop Computer Aided Diagnosis (CADx) system that can help radiologist determine breast cancer cases based on mammogram image. In this paper, a combination of the Gray-level Co-Occurrence matrix (GLCM) and Backpropagation Neural Network (BPNN) is used to classify normal-abnormal patient based on mammogram image. Using mammogram image provided by Mammography Imaging Analysis Society (MIAS), a test for the proposed method was concluded. The result was, accuracy 94.06%, Sensitivity 90.16% , and Specificity 95.57%.
关键词：Breast Cancer;Mammography;MIAS;GLCM;BPNN