GR-334 Comparative Evaluation of EMBED Dataset for Mammogram Classification Using Deep Learning Techniques

GR-334 Comparative Evaluation of EMBED Dataset for Mammogram Classification Using Deep Learning Techniques

Breast cancer is a global health concern for women. The detection of breast cancer in its early stages is crucial, and screening mammography serves as a vital leading-edge tool for achieving this goal. In this study, we evaluated the performance of centralized versions of Resnet 50v2 and Resnet 152v2 models for classification of mammograms using different datasets, which were divided by location number extracted from the EMBED dataset. The datasets were preprocessed and used various techniques to improve the performance of the models. The models are trained and evaluated using metrics such as accuracy, area under the curve (AUC), F1 score, precision, and recall. The results indicate good performance for both models, with the Resnet 152v2 model slightly outperforming the Resnet 50v2 model in terms of AUC score. Our findings demonstrate the potential of machine learning algorithms in breast cancer screening, with our model achieving an AUC score of 0.83.