Modeling the process of dermatological disease recognition in images using fuzzy logic

Recognition of dermatological diseases based on images is an important task in the field of medical diagnostics. Traditional image analysis methods, particularly those based on deep neural networks, often face challenges under conditions of low input data quality and variability of external factors such as lighting or blurriness. This study explores the modeling of a fuzzy logic system to improve the accuracy of dermatological disease diagnosis. A model is proposed that utilizes fuzzy variables and rules for image processing, enabling the consideration of uncertainty and data incompleteness. The structure of a controller with a fuzzy inference module is described, membership functions for key variables are developed, and a knowledge matrix for decision-making is constructed. The results of the study demonstrate that the application of fuzzy logic significantly enhances the accuracy and reliability of the image-based dermatological disease recognition process.