Generic hybrid model for breast cancer mammography image classification using EfficientNetB2

Abstract

Breast cancer is a global health issue that necessitates precise classification for early detection and effective treatment. In recent years, pre-trained models have shown great potential in the field of medical image classification, including breast cancer classification. These models have been trained on extensive datasets, and they possess the ability to capture intricate features and patterns within medical images, facilitating accurate classification. However, some of the models are non-generic. They can be sensitive to dataset biases, leading to over fitting on specific patterns present in the training data, and they equally struggle to handle data from different distributions. In this work, we proposed a generic hybrid model for image classification. The features were extracted from two datasets: the mammographic image analysis society (MIAS) and the INbreast dataset, respectively, through the pre trained EfficientNetB2 architecture. However, three classifiers were used in the image classification of the extracted features: MGSVM, CUBIC SVM, and XGBOOST. Eight evaluation metrics were selected to assess the performance of the proposed models. These metrics include accuracy, precision, F1-score, AUC, sensitivity, false negative rate (FNR), Kappa score, and time complexity. Experimental results show that the hybrid of EfficientNetB2 and the MGSVM classifier is more generic and efficient for breast cancer diagnosis and classification. It exhibits a strong performance when classifying mammography breast images from both datasets, achieving impressive metrics such as an overall accuracy of 99.47%, a sensitivity rate of 99.31%, precision of 99.44%, F1-score of 99.44%, AUC of 99.44%, a low FNR (False Negative Rate) of 0.007, a kappa score of 0.98, and a manageable time complexity of 231.44 seconds on the MIAS Dataset.