CFI-Net: A Choquet Fuzzy Integral Based Ensemble Network With PSO-Optimized Fuzzy Measures for Diagnosing Multiple Skin Diseases Including Mpox

In the domain of medical diagnostics, precise identification of various skin and oral diseases is vital for effective patient care. In particular, Mpox is a potentially dangerous viral disease with zoonotic origins, capable of human-to-human transmission, underscoring the urgency of precise diagnostic methods for timely intervention. This paper introduces a novel approach named the Choquet Fuzzy Integral-based Ensemble (CFI-Net) for accurate classification of skin diseases, with a specific emphasis on detecting Mpox, foot ulcers, and various mouth and oral diseases. Our methodology begins with Transfer Learning, enhancing the classification capabilities of base classifiers (DenseNet169, MobileNetV1 and DenseNet201) by incorporating additional layers. Subsequently, we aggregate the prediction scores from each base classifier using the Choquet fuzzy integral (CFI) to derive the final predicted labels, thus ensuring dynamic and robust predictions. Fuzzy measures, a crucial component of this fuzzy integral-based ensemble method, are typically determined through manual experimentation in previous approaches. However, in our study, we have tackled the challenge of manual tuning by employing meta-heuristic optimization algorithm to precisely configure the fuzzy measures for optimal performance. A rigorous evaluation is conducted on four publicly available datasets, encompassing two Mpox datasets, a foot ulcer dataset, and a mouth and oral disease dataset. The experiments reveal the remarkable effectiveness of CFI-Net in significantly improving disease classification accuracy. Additionally, we employ Grad-CAM analysis to provide insights into the decision-making processes of our models. Our findings underscore the exceptional performance of CFI-Net, achieving accuracy rates of 98.06% and 94.81% for Mpox detection, 99.06% for foot ulcer detection, and an impressive 99.61% for mouth and oral disease classification. This research not only contributes to the advancement of disease diagnosis but also demonstrates the effectiveness of ensemble learning techniques coupled with fuzzy integral-based fusion in enhancing diagnostic accuracy.