Advancing Dermatological Diagnostics: A Comparative Analysis of CNN Models in Skin Disease Classification

This comprehensive study presents a pioneering analysis of Convolutional Neural Network (CNN) models for the accurate classification of a wide array of dermatological conditions, which affect a significant portion of the global population and pose notable diagnostic challenges. These challenges stem from intricate visual factors in skin images, such as texture complexities, lesion locations, and the presence of hair, all of which are critical in diagnosing over 1500 identified skin disorders that considerably impact the quality of life. To address these challenges, this study proposes three distinct CNN models, analyzing pixel-level data from an extensive dataset. The first model achieves an accuracy of 82%, while the second and third models achieve accuracy rates of 78% and 81% respectively. To further refine diagnostic precision, a voting ensemble mechanism is implemented, synergistically consolidating predictions from all three models. This ensemble strategy surpasses the performance of the individual models, achieving an accuracy rate of 92%. The research makes a significant leap in dermatological classification, showcasing the superiority of CNN models in handling complex visual cues and diverse skin conditions, and ultimately contributing notably to advancements in dermatological diagnostics with its innovative approach and high diagnostic accuracy.