Skin Disease Detection Using Convolutional Neural Networks

Skin diseases continue to be one of the most widely diagnosed medical concerns globally, affecting both developed and developing regions at scale. These dermatological conditions, which range from fungal infections and viral lesions to chronic autoimmune disorders, demand timely and precise identification to ensure effective treatment. Unfortunately, diagnosis often hinges on access to skilled dermatologists and clinical resources, both of which may be limited in rural or underserved areas. Manual diagnosis is not only time-consuming but prone to errors due to visual overlap across diseases. To address this challenge, our research presents a robust deep learning-based classification system that uses Convolutional Neural Networks (CNNs) to automatically detect a wide range of skin diseases from image data. We compiled a diverse and highresolution custom dataset consisting of over 8,000 annotated images covering various dermatological conditions including Chickenpox, Shingles, Psoriasis, Nail Fungus, Cutaneous Larvae Migrans, Impetigo, and several others. We trained a ResNet50-based model, which leveraged transfer learning and advanced preprocessing strategies to enhance classification performance. Our model attained over 92% accuracy, demonstrating high generalization capability. In this paper, we explore the dataset preparation process, CNN design, training methodology, and evaluation metrics in-depth. Furthermore, we integrate state-of-the-art explainability techniques and discuss practical deployment strategies to ensure real-world applicability. The results strongly support CNNs as reliable tools for aiding dermatological diagnostics in both clinical and mobile telemedicine applications.