Document Type : Original Article
Authors
1 Department of Computer Engineering, Ra.C., Islamic Azad University, Rasht, Iran
2 Department of Electrical Engineering, Ra.C., Islamic Azad University, Rasht, Iran
Abstract
Early detection and diagnosis of breast cancer masses can significantly reduce mortality associated with this disease. This paper introduces a system designed to identify cancerous masses in mammography images. The proposed system is composed of four phases: pre-processing, feature extraction, feature selection, and classification. In the pre-processing phase, the region of interest is isolated from the image, noise is eliminated using a median filter, and contrast is enhanced through histogram adjustment. In the feature extraction phase, the features pertinent to shape, histogram, and texture are extracted from the region of interest. In the third phase, the dual-objective coral reef algorithm is employed for feature selection. Finally, an SVM classifier is utilized in the classification phase. The proposed method was applied to 100 images from the MIAS database and 300 images from the CBIS-DDSM database. Based on the quantitative results, the system demonstrated promising performance in reducing classification errors. its accuracy, sensitivity, specificity, AUC, MCC, and F1-Score values for the MIAS database were calculated as 95.4%, 99.18%, 91.79%, 0.91, 0.95, and 95.32%, respectively, and for the CBIS-DDSM dataset database were calculated as 96.73%, 98%, 95.46%, 0.934, 0.968, and 96.71%, respectively.
Keywords
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