Automatic Fuzzy Graph Construction For Interpretable Image Classification

Abstract

Interpretable machine learning models have recently received a considerable attention for their capability to provide understandable explanations of predictions evoked from complex systems. In this paper a novel automatic interpretable classification scheme is introduced based on a Fuzzy Cognitive Map (FCM). The proposed approach aims to address the problem of image classification using highlevel features, extracted from a Convolutional Neural Network (CNN). The proposed FCM constitutes a fuzzygraph structure for representing causal reasoning concerning semantic concepts of the real world, as these are depicted within different images. An advantage over current classifiers is that the proposed FCM embeds a mechanism for automatic determination of its structure from the datasets used in each experiment. More importantly, it provides interpretable classification results, whereas it is simple to implement. Experimental results using publicly available datasets show that the proposed approach efficiently extends the application of the traditional FCMs into image classification problem.

Explainable Deep Fuzzy Cognitive Map Diagnosis of Coronary Artery Disease: Integrating Myocardial Perfusion Imaging, Clinical Data, and Natural Language Insights

“…The strengths and directions of connections, represented by weighted edges, enable the representation of expert knowledge and domain expertise, making FCMs particularly suitable for medical applications [12][13][14]. Recent publications, such as those by Sovatzidi et al [15][16][17], have further advanced the field by enabling the processing of images within the FCM framework. They achieved this using transfer learning and K-means clustering, thereby opening new avenues for more transparent and explainable models in medical domains, including CAD.…”