Breast Cancer Detection and Diagnosis Using Gabor Features and EfficientNetV2 Model

Breast cancer is one of the most common diseases and the second most dangerous and fatal disease after skin cancer. The malignant masses begin to grow in the breast cells and develop in the breast tissue of women all over the world. Due to the necessity of early diagnosis to prevent the development of cancer and thus improve the chances of survival, many computer-aided methods are available to automate tissue classification to reduce the workload of the pathologist and improve accuracy. In this paper, a proposed method Gabor-EfficientNetV2 to extract deep features from a breast cancer dataset called “Breast cancer Histopathological dataset” (BreaKHis) to classify it as “benign” or “malignant” based on Gabor filter bank and EfficientNetV2 architecture. The proposed Gabor-EfficientNetV2 model offers significant advances in breast cancer detection and diagnosis by combining the texture analysis capabilities of Gabor filters with the efficiency and scalability of the EfficientNetV2 architecture. This hybrid approach enhances feature representation, resulting in improved classification accuracy and robustness compared to traditional models. Highest classification accuracy obtained with 400X magnification factor where the train accuracy of 97% and train loss of 0.13% while the test accuracy of 96.3% and the test loss of 0.135%, precision of 97.30% , recall of 90.30% , and F1-score obtained is 98.52%.