Development of a Multi-Dimensional Image Processing Model for Personalized Diagnosis and Severity Assessment of Skin Diseases

Skin diseases affect millions worldwide, and more than 3,000 different conditions present significant challenges to diagnose and suggest treatments. This study introduces a novel multi-dimensional image processing model that gives three critical outputs: skin type classification, Skin disease prediction, and severity level assessment. Unlike the traditional image-only diagnostic models, this approach puts forward a customized convolutional neural network (CNN) architecture that utilizes high-resolution dermatological images with clinical data, including patient demographics, and symptomatology, to provide a personalized and accurate diagnostic solution. Considering the variation in skin type and disease manifestation, the model has been adapted to adjust its prediction based on the unique characteristics of every patient. The final model, trained on such a diverse and clinically representative dataset, includes the diagnosis based on the severity—(mild, moderate, or severe)—and skin types, such as dry, oily, or normal, which represents critical gaps in current AI applications. The developed model could be implemented in real-time clinical use, as it is scalable and data-driven, effectively addressing diagnostic challenges. It is particularly beneficial in resource-constrained settings, offering accurate and tailored skin disease diagnostics to meet individual patient needs.