Breast lesion classification via colorized mammograms and transfer learning in a novel CAD framework.

Medical imaging sciences and diagnostic techniques for Breast Cancer (BC) imaging have advanced tremendously, particularly with the use of mammography images; however, radiologists may still misinterpret medical images of the breast, resulting in limitations and flaws in the screening process. As a result, Computer-Aided Design (CAD) systems have become increasingly popular due to their ability to operate independently of human analysis. Current CAD systems use grayscale analysis, which lacks the contrast needed to differentiate benign from malignant lesions. As part of this study, an innovative CAD system is presented that transforms standard grayscale mammography images into RGB colored through a three-path preprocessing framework developed for noise reduction, lesion highlighting, and tumor-centric intensity adjustment using a data-driven transfer function. In contrast to a generic approach, this approach statistically tailors colorization in order to emphasize malignant regions, thus enhancing the ability of both machines and humans to recognize cancerous areas. As a consequence of this conversion, breast tumors with anomalies become more visible, which allows us to extract more accurate features about them. In a subsequent step, Machine Learning (ML) algorithms are employed to classify these tumors as malign or benign cases. A pre-trained model is developed to extract comprehensive features from colored mammography images by employing this approach. A variety of techniques are implemented in the pre-processing section to minimize noise and improve image perception; however, the most challenging methodology is the application of creative techniques to adjust pixels' intensity values in mammography images using a data-driven transfer function derived from tumor intensity histograms. This adjustment serves to draw attention to tumors while reducing the brightness of other areas in the breast image. Measuring criteria such as accuracy, sensitivity, specificity, precision, F1-Score, and Area Under the Curve (AUC) are used to evaluate the efficacy of the employed methodologies. This work employed and tested a variety of pre-training and ML techniques. However, the combination of EfficientNetB0 pre-training with ML Support Vector Machines (SVM) produced optimal results with accuracy, sensitivity, specificity, precision, F1-Score, and AUC, of 99.4%, 98.7%, 99.1%, 99%, 98.8%, and 100%, respectively. It is clear from these results that the developed method does not only advance the state-of-the-art in technical terms, but also provides radiologists with a practical tool to aid in the reduction of diagnostic errors and increase the detection of early breast cancer.