Explainable Classifier Supporting Decision-making for Breast Cancer Diagnosis from Histopathological Images

This paper presents an application of semantically explainable classifier, Cumulative Fuzzy Class Membership Criterion (CFCMC), in medical domain, specifically for breast cancer detection from histopathological images. This classifier, in contrast with commonly used classifiers for image classification, is able to provide additional information about its classification results in human-friendly form. In this paper, we proposed a means for presenting the additional semantical informations that is potentionally useful for decision-making in medical domain. First, the classifier provides semantic explanation, regarding the possibility of misclassification of the test sample. Alongside with semantics, it provides visualization of similar and non-similar samples of different class. The classification performance of CFCMC is compared against three commonly used classifiers for image classification, Convolutional Neural Network (CNN), Stacked Auto-encoder (SAE) and Deep Multi-layered Perceptron. The experimental result shows that the CFCMC is not necessarily the best classifier. However, the ability to provide semantic and visual explanation of classification result allows the classifier to be applied as a supporting tool for pathologists in diagnostic of breast cancer.