Gamma function based ensemble of CNN models for breast cancer detection in histopathology images

Breast cancer is the second deadliest disease amongst women worldwide. Breast histopathology image analysis is one of the most powerful ways used for the detection of tumour malignancies. Manual breast histopathology image analysis is, however, subjective, time-consuming and prone to human errors. Computer-aided diagnosis (CAD) has become a popular and viable solution for medical image analysis due to recent advances in computer power and memory. However, the performance of the CAD models needs to be improved to use for practical purposes. Convolutional neural network (CNN) based models have achieved promising results for breast histopathological image classification. In this paper, instead of relying on a single CNN model, we have proposed a novel rank-based ensemble method by combining outcomes of three transfer learning CNN models, namely GoogleNet, VGG11 and MobileNetV3_Small. The proposed ensemble model is designed using the Gamma function for solving a 2-class classification problem of breast histopathological images. In comparison to state-of-the-art approaches, our method produces better classification results, with 99.16%, 98.24%, 98.67%, and 96.16% for 40X, 100X, 200X, and 400X levels of magnification, respectively, on a publicly accessible standard dataset called BreakHis and 96.95% on another well-known dataset called ICIAR-2018.