Skin Sure : Real-Time AI for Skin Health

Abstract

Skin ailments are among the most frequently occurring health issues worldwide, commonly causing not only physical complications but also neuropsychological distress and, in severe cases, skin cancer. Traditional diagnosis by dermatologists is time-intensive, subjective, and susceptible to inaccuracies, emphasizing the urgent need for algorithm-based, reliable, and accurate diagnostic systems. From this work, we propose a computer-aided paradigm for skin disease identification and categorization employing advanced image processing and machine learning approaches. Preprocessing steps such as digital hair removal, denoising, and optimized segmentation are applied to enhance lesion visibility. Feature extraction is carried out using texture descriptors including GLCM-based and statistical feature extraction methods measures, while dimensionality reduction is achieved through latent representations. For classification, we integrate machine learning models including Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), and deep learning approaches including Convolutional Neural Networks (CNN) and autoencoders. The proposed methodology is validated on benchmark datasets such as ISIC and HAM10000, demonstrating improved accuracy and robustness compared to traditional strategies. This framework not only supports dermatologists in early and precise diagnosis but also paves the way for real-time, scalable, and individualized skin disease recognition systems.

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