Enhanced detection of female breast cancer from digital mammography employing transfer deep learning neural networks

Abstract

Early detection of breast cancer (BrC) is one of the best strategies to prevent the disease's spread. This makes an autonomous diagnosis system based on deep learning (DL) attractive for improving the accuracy of detection and prediction. This study suggests employing transfer DL models to categorize BrC from mammograms. Furthermore, to identify BrC detection architectures, transfer DL models are applied to various well-known convolutional neural networks (CNNs). Three CNNs (NasNetMobile, EfficientNet-b0, and MobileNetV2) are adjusted in particular ways before being used. All systems use two types of optimizers: root mean square propagation (RMSP) and adaptive moment estimation (ADAM). The EfficientNet-b0 network attains 96.45% accuracy, 96.63% sensitivity, and 97.18% F1-score when using the ADAM optimizer. The experimental results demonstrate that EfficientNet-b0 outperforms other sophisticated CNN techniques and offers a number of advantages. Additionally, the EfficientNet-b0 obtained an F1-score of 96.00%, a sensitivity of 96.55%, and an accuracy of 95.04% utilizing the RMSprop optimizer. To sum up, this work improves the identification of BrC by applying transfer DL models to digital mammography scans. The best-performing CNN among the three (NasNetMobile, EfficientNet-b0, and MobileNetV2) was EfficientNet-b0 optimized with ADAM and RMSprop. These results show how these structures could improve healthcare and increase the accuracy of BrC detection.