Segmentation and Classification of Breast Masses From the Whole Mammography Images Using Transfer Learning and BI-RADS Characteristics

Breast cancer is the most prevalent cancer among women worldwide, highlighting the critical need for its accurate detection and early diagnosis. In this context, the segmentation of breast masses (the most common symptom of breast cancer) plays a crucial role in analyzing mammographic images. In addition, in image processing, the analysis of mammographic images is very common, but certain combinations of mathematical tools have never been exploited. We propose a computer-aided diagnosis (CAD) system designed with different and new algorithm combinations for the segmentation and classification of breast masses based on the Breast Imaging-Reporting and Data System (BI-RADS) lexicon. The image is initially divided into superpixels using the simple linear iterative clustering (SLIC) algorithm. Fine-tuning of ResNet50, EfficientNetB2, MobileNetV2, and InceptionV3 models is employed to extract features from superpixels. The classification of each superpixel as background or breast mass is performed by feeding the extracted features into a support vector machine (SVM) classifier, resulting in an accurate primary segmentation for breast masses, refined by the GrabCut algorithm with automated initialization. Finally, we extract contour, texture, and shape parameters from the segmented regions for the classification of masses into BI-Rads 2, 3, 4, and 5 using the gradient boost (GB) classifier while also examining the impact of the surrounding tissue. The proposed method was evaluated on the INBreast database, achieving a Dice score of 87.65% and a sensitivity of 87.96% for segmentation. For classification, we obtained a sensitivity of 88.66%, a precision of 90.51%, and an area under the curve (AUC) of 97.8%. The CAD system demonstrates high accuracy in both the segmentation and classification of breast masses, providing a reliable tool for aiding breast cancer diagnosis using the BI-Rads lexicon. The study also showed that the surrounding tissue has an impact on classification, leading to the importance of choosing the right size of ROIs.