An Efficient Transfer and Ensemble Learning Based Computer Aided Breast Abnormality Diagnosis System

Abstract

Breast cancer is the second most deadly type of cancer globally among women and can be prevented to a great extent in the case of early detection. In order to raise the survival rate, research scientists have conducted several experiments to develop tools to alleviate this problem, including Computer-Aided Diagnosis (CADx) systems. Deep Learning and its important sub-field Convolutional Neural Networks (CNN)s have revolutionized (CADx) development research. While the Curated Breast Imaging Subset of Digital Database for Screening Mammography, or the CBIS-DDSM dataset, has been classified using different pre-trained architectures, few of them have used ensemble learning to provide a more robust and accurate architecture. To the best of our knowledge, we are the first to integrate the application of the state-of-the-art pre-trained model called EfficientNet along with other pre-trained models for the part, and subsequently, the models were concatenated (ensembled). With the application of pre-trained CNN-based models, we are able to address the problem of not having a large dataset. Nevertheless, with the EfficientNet family offering better results with fewer parameters, we obtained significant improvement in accuracy, and later ensemble learning was applied to provide robustness for the network. After performing 10-fold cross-validation, our experiments yielded promising test accuracy results, 96.05% and 85.71% for abnormality type and pathology diagnosis classification, respectively.

Keywords

• Mammogram classification
• breast cancer
• medical imaging
• computer-aided diagnosis (CADx)