Breast mass density categorisation using deep transferred EfficientNet with support vector machines

Breast carcinoma or breast cancer is the most common type of cancer in women. It is very dangerous, and early detection is important for proper diagnosis and reduction of mortality rate. Cancer starts in a benign state, and if there is no proper treatment, it becomes malignant. Computer-aided diagnosis (CAD) techniques can be used for early detection of cancers. These techniques can significantly and effectively help in the diagnosis of cancer. Deep learning is the most popular technique used for accurate data analysis. It is also a powerful tool for categorizing histopathological images of breast cancer and analyzing breast cell shapes and densities. This paper proposes a hybrid EfficientNet with an SVM to categorize breast mass density with consideration of tumor types. (i.e., malignant or benign). In this hybrid, EfficientNet, the mammography images are applied to EfficientNet, and then the classification is carried out using a Support Vector Machine (SVM). The proposed model achieved accuracy of 94.48%, precision of 94.79%, specificity of 99.21%, Precision of 94.79%, FPR of 0.79%, score of 94.45%, MCC of 93.78%, and Kappa of 74.76%. The model outperformed state-of-the-art techniques.