Detection and classification of microcalcifications in mammograms images using difference filter and Yolov4 deep learning model

In this study, we focus on increasing the visibility of microcalcifications (MCs) in mammogram images by means of the difference filter and classifying the visibility-increased MCs by using Yolov4 deep learning model. The same classification experiments are reperformed for also the widely used Faster R-CNN deep learning model to compare with the proposed approach. For this aim, the difference filter is applied to the sections taken from normal and abnormal labeled mammogram images, and the filtered images are used as inputs to Yolov4 and Faster R-CNN models in order to classify as normal and abnormal. In order to show the contribution of the difference filter to the classification success, the experiments are reimplemented without using the difference filter. The difference filter based on the neighborhood relations of the image pixels significantly improves the classification success ratios of the classifier models used in the study since it increases especially the visibility of the rounded edges and makes microcalcifications in the image more prominent. As a result, the experiments show that the use of deep learning models together with the difference filter contributes significantly to the classification success. Finally, this study gives rise to the idea that it can greatly contribute to studies reading of the mammograms with MCs (abnormal) highlighted by the use of difference filter.