LARGEST ROI SEGMENTATION FOR BREAST CANCER CLASSIFICATION USING A VGG16 DEEP LEARNING NETWORK

The exact evaluation of breast cancer im- ages for patients is very important, because they can be early treated for lasting their life. This article pro- poses a classification system for finding breast cancer images, in which each breast lesion image is segmented to produce a largest Region of Interest (ROI) and a VGG16 deep learning network is applied for classifi- cation. An Otsu threshold is utilized on two datasets from two sources of CBIS-DDSM and MIAS to create largest ROI with main features. For the classification with high performance, two datasets of the breast le- sions were augmented by rotating, flipping, and bright- ness variation. This article was proposed an algorithm with processing images sets before classification using VGG16. In particular, the results of the largest ROI datasets for four types of breast lesions were repre- sented through segmentation, normalization and en- hancement. In addition, the results of classifying four types of breast lesions (BC, BM, MC, MM) were eval- uated using confusion matrix, with the high accuracy of around 95%. Another evaluation was that these im- age sets without ROI/with ROI parts/With the largest ROI only using the Otsu segmentation were compared and the highest accuracy was of the image sets with the largest ROI. The results with the high accuracy demon- strated to illustrate the effectiveness of the proposed method. It means that this method can be developed to classify many stages of breast cancers during diagnosis and treatment.