SkinSense AI: AI Based Skin Disease Diagnosis

Abstract

Skin diseases affect a large part of the global population, and quick detection is essential for preventing serious complications and improving treatment outcomes. In many places, diagnosis still relies on traditional visual examinations by dermatologists. This process can be time-consuming, subjective, and hard to access in areas with limited medical facilities. Although automated diagnostic methods have been explored, reliable detection, especially for early-stage conditions and visually similar diseases, is still challenging.

This study proposes a deep-learning-based skin disease classification system that aims to distinguish between two categories using medical image data(Melanoma and Eczema). A labeled dataset of skin images was used to train a convolutional neural network (CNN). The workflow includes image preprocessing, data augmentation to improve dataset diversity, the use of transfer learning for better feature extraction, and a thorough evaluation of the model based on accuracy, precision, recall, and confusion matrix metrics.

The experimental results show that the developed model achieves strong classification performance, proving its effectiveness as a preliminary screening tool. These findings emphasize the potential of artificial intelligence to help healthcare professionals by enabling faster detection, reducing diagnostic effort, and improving access to initial assessments in remote and resource-limited areas. With further refinements, the system could be expanded to support multi-class disease recognition, mobile-based real-time diagnosis, and wider clinical use.