Deep Learning Models for Skin Disease Classification: A DenseNet Perspective

Abstract: Skin diseases represent a considerable public health challenge worldwide, affecting people of all ages and demographics. Failure to detect and treat these diseases early can result in severe complications, both medical and psychological. While dermatologists traditionally rely on visual assessments and patient history for diagnosis, this approach can be subjective and time-consuming. In recent years, the use of artificial intelligence, particularly deep learning, has gained traction in medical image analysis due to its ability to automate complex classification tasks. This paper proposes an effective approach for classifying skin conditions using DenseNet121—a convolutional neural network pre-trained on ImageNet. Through transfer learning and fine-tuning, we adapt DenseNet121 for skin disease classification using an annotated dataset of dermatological images. Our proposed framework is evaluated based on several performance metrics including accuracy, precision, recall, and F1-score. The results underscore the potential of the model in real- world clinical scenarios, especially in resource-limited settings.