Towards a Support System for Digital Mammogram Classification

Abstract

Cancer is the illness of the 21th century. With the development of technology some of these lesions became curable, if they are in an early stage. Researchers involved with image processing started to conduct experiments in the field of medical imaging, which contributed to the appearance of systems that can detect and/or diagnose illnesses in an early stage. This paper’s aim is to create a similar system to help the detection of breast cancer. First, the region of interest is defined using filtering and two methods, Seeded Region Growing and Sliding Window Algorithm, to remove the pectoral muscle. The region of interest is segmented using k-means and further used together with the original image. Gray-Level Run-Length Matrix features (in four direction) are extracted from the image pairs. To filter the important features from resulting set Principal Component Analysis and a genetic algorithm based feature selection is used. For classification K-Nearest Neighbor, Support Vector Machine and Decision Tree classifiers are experimented. To train and test the system images of Mammographic Image Analysis Society are used. The best performance is achieved features for directions {45◦ , 90◦ , 135◦ }, applying GA feature selection and DT classification (with a maximum depth of 30). This paper presents a comprehensive analysis of the different combinations of the algorithms mentioned above, where the best performence repored is 100% and 59.2% to train and test accuracies respectively. Received by the editors: 22 June 2021. 2010 Mathematics Subject Classification. 68T35. 1998 CR Categories and Descriptors. I.2.1 [Artifical Intelligence]: Applications and Expert Systems – Medicine and science; I.2.6 [Artifical Intelligence]: Learning – Knowledge acquisition; I.4.6 [Image Processing and Computer Vision]: Segmentation – Pixel classification; I.4.7 [Image Processing and Computer Vision]: Feature Measurement – Feature representation;

Keywords

• region growing, k-means, GLRLM feature extraction, GA feature selection, PCA, mammogram, classification, Decision Tree classification, Random Forest classification, MIAS.