mBCCf: Multilevel Breast Cancer Classification Framework Using Radiomic Features

Breast cancer characterization remains a significant and challenging issue in contemporary medicine. Accurately distinguishing between malignant and benign breast lesions is crucial for effective diagnosis and treatment. The anatomical structure of malignant breast ultrasound images is more chaotic than that of benign images due to disease pathologies. However, texture-based analysis alone often fails to identify the extent of chaoticness in malignant breast ultrasound images due to their vague appearance with normal echo patterns, leading to missed diagnoses and increased mortality rates. To address this issue, we proposed an angular feature-based multilevel breast cancer classification framework mBCC(f) that aims to improve the accuracy and efficiency of classification. The proposed framework mimics the radiologist interpretation procedure by identifying the chaoticness on the periphery of the breast lesion in a breast ultrasound image (level-1). If the lesion contains an acute angle in any part of the periphery, it can be characterized as malignant or otherwise benign. However, solely relying on level-1 analysis may result in misclassification, especially when benign lesions exhibit echo patterns that resemble malignant ones. To overcome this limitation and to make the proposed system highly sensitive, advanced texture-based analysis (using combined shape, texture, and angular features) is performed (level-2). Finally, the performance of the proposed system is evaluated using a cross-dataset (consisting of 1293 breast ultrasound images) and compared with the different individual feature extraction techniques. Encouragingly, our system demonstrated an accuracy of 96.99% for classifying malignant and benign tumors, which is also validated using statistical analysis. The implications of our research lie in its potential to significantly improve breast cancer diagnosis by providing a reliable, efficient, and sensitive tool for radiologists.