Open AccessAnalysis of Tissue Abnormality in Mammography Images Using Gray Level Co-occurrence Matrix Method
Author(s) -
Mohammed Y. Kamil,
Abdul-Lateef A. Jassam
Publication year - 2020
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 - 10.1088/1742-6596/1530/1/012101
Subject(s) - mammography , false positive paradox , gray level , abnormality , breast cancer , pattern recognition (psychology) , artificial intelligence , true positive rate , co occurrence matrix , cad , classifier (uml) , medicine , computer science , radiology , cancer , image processing , image (mathematics) , psychiatry , image texture , engineering drawing , engineering
One of the dangerous diseases is breast cancer, which threatens women and men to the same extent. But women are more affected by this disease. Computer-Aided Diagnosis (CAD) is the optimal method for the early detection of breast cancer. It can reduce the false positives in radiologist diagnosis, which leads to reduce the death-rate. This paper presents a feature extraction technique with mammography images to breast mass recognition. Then, distinguishing normal tissue and abnormal breast masses. The mini-MIAS database of mammograms was used in this paper. Gray Level Co-occurrence Matrix is the method that was used to extract features from the region of interest. The best sensitivity, specificity, and accuracy are observed with a k nearest neighbor classifier.