Revolutionizing Dermatology with High-Accuracy Skin Condition Classification Through ResNet50

The biggest organ in the human body, the skin protects us immensely and is prone to several diseases that compromise its look and condition. This article explores the ResNet50 model for use in skin condition categorization as a deep convolutional neural network with a deep residual learning architecture. Drawn from Kaggle, the 2,394-image collection including six classes—Acne, Carcinoma, Eczema, Keratosis, Milia, and Rosacea—sourced from Every class comprises 399 images, which enables fast evaluation and training for the model. Strictly separated between test sets, validation, and training, the dataset guarantees excellent model evaluation. Among more assessments of the model's categorizing capacity are accuracy, precision, recall, and F1-score. When separating among many skin conditions, the ResNet50 model exhibits outstanding 92% classification accuracy. The outstanding performance of the model emphasizes its practical importance in clinical settings, where it can significantly improve diagnostic accuracy, enable individualized treatment regimens, and improve dermatological care by means of noteworthy rise of diagnosis accuracy. Stressing the changing power of modern artificial intelligence technologies on dermatology, this work offers the route for more accurate and simply accessible skin condition classification.