A b s t r a c t



Vitiligo is an autoimmune skin disorder characterized by the loss of pigmentation and the appearance of white patches on the skin. Beyond its physical manifestations, vitiligo imposes significant psychological and social impacts on affected individuals, making rapid and accurate diagnosis critically important. This study introduces a novel approach for vitiligo diagnosis by integrating fuzzy logic systems with Support Vector Machine (SVM) algorithms, along with precise optimization of model parameters. Input data were collected from clinical observations and physician reports, and after preprocessing, were fed into the fuzzy system. In this stage, effective diagnostic features were extracted through careful tuning of membership functions and fuzzy rules. These features were then passed to the SVM model, where decision boundaries were optimized to significantly enhance diagnostic accuracy.

For initial evaluation, the proposed model was tested on the public dataset “Vitiligo and Healthy Images,” comprising approximately 3,628 clinical images of vitiligo patients and healthy individuals. Additionally, clinical data from 100 patients were independently collected and assessed using 10-fold cross-validation. The fuzzy system and SVM model were implemented using MATLAB and Python. Results demonstrated that the proposed model achieved a diagnostic accuracy of 93%, outperforming or matching advanced deep learning models such as Swin Transformer and CNN architectures like VGG19 and ResNeXt101. Moreover, the use of fuzzy logic improved the interpretability of the model and enhanced its robustness against uncertain data—features that make it a practical and efficient solution for clinical applications.