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Transfer learning and fine tuning in mammogram bi-rads classification

Abstract:

The BI-RADS report system is widely used by radiologists and clinicians to document relevant findings in the mammogram exam by using a 6 category final assessment. Deep learning has achieved a high level of accuracy in multi category classification of natural images. Because of that, it is of interest to address the mammography malignancy classification according to the established BI-RADS categories. In this work, we use transfer learning on NASNet Mobile and fine tuning on VGG16 and VGG19 to classify mammogram images according to the BI-RADS scale on the INbreast dataset. Our proposed methodology achieved an accuracy (ACC) of 90.9% and a macro averaged area under the receiver operating characteristic curve (AUC) of 99.0%; outperforming some of the similar works found in the literature review.