Experimental Investigation of Classification Algorithms for Predicting Lesion Type on Breast DCE-MR Images

Timely revealing of breast cancer is one of the most important issues in determining prognosis for women with malignant tumors. Dynamic contrast-enhanced (DCE) MRI is being increasingly used in the clinical setting to help detect and characterise tissue, suspicious for malignancy and has been shown to be the most sensitive modality for screening highrisk women. Computer-assisted evaluation (CAE) systems have the potential to assist radiologists in the early detection of cancer. A crucial module of the development of such a CAE system will be the selection of an appropriate classification function responsible for separating malignant and benign lesions. The motivation of this paper is to provide qualitative evaluation of three advanced classifiers like artificial neural network, support vector machine and artificial bee colony optimization algorithm trained neural network are being developed for classification of the suspicious lesions in breast MRI. A comparative study of these techniques for lesion classification is made to identify relative merits. As a result, the paper concluded that the neural network trained by artificial bee colony optimization algorithm based classifier outperforms all other explored classifiers for the examined dataset of breast DCE -MR images.