Open AccessThe application of statistic image analysis for classification of breast cancer based on mammograms
Author(s) -
Dwi Ratna Sulistyaningrum,
Budi Setiyono,
Daryono Budi Utomo,
Bandung Arry Sanjoyo
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1218/1/012040
Subject(s) - artificial intelligence , thresholding , computer science , pattern recognition (psychology) , preprocessor , gray level , feature extraction , image processing , segmentation , computer vision , image segmentation , feature (linguistics) , image (mathematics) , linguistics , philosophy
Cancer is a disease characterized by the ability of abnormal cells to grow uncontrollably. In the medical field, detection of breast cancer is done using a mammogram. Examination of the mammogram image is still done manually by the doctor / radiologist, so it is necessary to use technology as supporting information. In this research, mammogram image classification based on gray-level co-occurrence (GLCM) matrix and gray-tone difference matrix (GTDM) has been done with backpropagation method. The stages of the mammogram image classification process include the process of image acquisition, pre-processing, feature extraction with GLCM and GTDM and classification using backpropagation. The preprocessing process carried out is gray-scalling, contrast enhancement, image segmentation with Otsu thresholding, edge detection process, and image thickening process with widening morphology method. The highest performance results for accuracy are 85% and precision is 85.7%. This result was obtained when using the GLCM and GTDM feature extraction methods.