Authors:
Adél Bajcsi
;
Anca Andreica
and
Camelia Chira
Affiliation:
Babes, –Bolyai University, Cluj-Napoca, Cluj, Romania
Keyword(s):
Breast Lesion Classification, Mammogram Analysis, Shape Features, Random Forest, DDSM.
Abstract:
Proper treatment of breast cancer is essential to increase survival rates. Mammography is a widely used, noninvasive screening method for breast cancer. A challenging task in mammogram analysis is to distinguish between tumors. In the current study, we address this problem using different feature extraction and classification methods. In the literature, numerous feature extraction methods have been presented for breast lesion classification, such as textural features, shape features, and wavelet features. In the current paper, we propose the use of shape features. In general, benign lesions have a more regular shape than malignant lesions. However, there are exceptions and in our experiments, we highlight the importance of a balanced split of these samples. Decision Tree and Random Forest methods are used for classification due to their simplicity and interpretability. A comparative analysis is conducted to evaluate the effectiveness of the classification methods. The best results we
re achieved using the Random Forest classifier with 96.12% accuracy using images from the Digital Dataset for Screening Mammography – DDSM.
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