A compact fuzzy rule interpretation of SVM classifier for medical whole slide images

In this paper, we address the lack of interpretability of Support Vector Machine (SVM) via rules based on support vectors. The lack of intuitive explanation of the rules in the domain of medical whole slide image classification by determining a reduced subset of Scale-Invariant Feature Transform (SIFT) features and linear dimensionality reduction. This reduced subset of SIFT features that participate in decision making provide an intuitive interpretation of the decision rules via a visual presentation of the features in a slide image. In our work, we have taken whole-slide images of sentinel lymph node to predict the presence of cancer using SVM and developed an equivalent Fuzzy model for better interpretability. The SVM model consisted of 50 support vectors which achieved an accuracy of 94.59%. A two IF-THEN rules equivalent Fuzzy Rule Based Model (FRBS) was created using all 50 support vectors which classified each WSI in either {−1, 1} class for non-cancerous and cancerous respectively. FRBS achieved 91.89% accuracy which shows that it is able to represent SVM working accurately in a human interpretable way.