Optimizing Breast Cancer Detection: Integrating Few-Shot and Transfer Learning for Enhanced Accuracy and Efficiency

Abstract

Breast cancer (BC) detection based on mammogram images is still an open issue, particularly when there is little annotated data. Combining few-shot learning (FSL) with transfer learning (TL) has been identified as a potential solution to overcome this problem due to its ability to learn from a few examples while producing robust features for classification. The objective of this study is to use and analyze FSL integrated with TL to enhance the classification accuracy and generalization ability in a limited dataset. The proposed approach integrates the FSL models (prototypical networks, matching networks, and relation networks) with the TL procedures. The models are trained using a small set of samples with annotation and can be assessed using various performance metrics. The models were trained and compared to the TL and the state-of-the-art methods regarding accuracy, precision, recall, F1-score, and area under the ROC curve (AUC). The models proved to be effective when integrated, and the relation networks model was the most accurate, with an accuracy of 95.6% and an AUC of 0.970. The models provided higher accuracy, recall, and F1-scores, especially in the case of discerning between normal, benign, and malignant cases, as compared to TL traditional techniques and the various recent state-of-the-art techniques. This integrated approach gives high efficiency, accuracy, and scalability to the whole BC detection process, and it has potential for further medical imaging domains. Future research will explore hyperparameter tuning and incorporating electronic health record systems to enhance diagnostic precision and individualized care.