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Breast cancer is a serious health concern and one of the leading causes of cancer-related deaths among women worldwide. The early diagnosis of breast cancer is crucial for successful treatment and improved patient outcomes. Advanced computational techniques, particularly deep transfer learning have gained considerable attention for their effectiveness in medical image computing. Initially, six transfer learning models with different specifications are trained, validated and tested on the breast ultrasound image dataset. Additionally, a novel hybrid model, DRM-Net, is developed by stacking the top three TL models based on concatenation and flattening of deep dense layers. Various techniques, including image preprocessing, image masking, data augmentation, and hyperparameter tuning, are incorporated to enhance the overall performance of the considered models. The models are thoroughly evaluated using various standard metrics and statistical methods. Furthermore, to ensure transparent decision-making, the proposed model is interpreted using XAI-based class activation mapping (CAM) method with different versions. The proposed DRM-Net model demonstrated superior predictive performance compared to the other six transfer learning models. Among all the models, DRM-Net achieved the highest overall performance, with an accuracy of 96.71%, precision of 96%, recall of 97%, F1-score of 97%, and an AUC value of 99%, respectively. It also outperformed state-of-the-art similar studies in the literature. The experimental results demonstrate the potential utility of the hybrid DRM-Net model in improving the accuracy of breast cancer diagnosis, which could facilitate informed clinical decision-making. The proposed model can also be applied to other diseases where ultrasound imaging is available.
Ganie et al. (Fri,) studied this question.