Authors: G. Lavanya, B. Balaki, Dr. Bhuvana. R
Abstract: Skin diseases are among the most common health problems worldwide, affecting individuals regardless of age, gender, or geographical location. Early detection and appropriate treatment are essential to prevent complications and psychological distress. However, limited access to dermatologists, especially in rural and underserved areas, delays timely diagnosis. This paper presents SkinGuard AI, a deep learning-based dermatology assistant that utilizes a Convolutional Neural Network (CNN) for image-based skin disease classification. The system enables users to upload images of affected skin areas through a web interface, where the images are preprocessed and analyzed using a trained CNN model. The system predicts the disease category along with a confidence score and provides personalized treatment recommendations. Additionally, it integrates an intelligent chatbot for interactive assistance and an automated email notification module to send diagnostic reports to registered guardians. The proposed solution enhances accessibility, reduces dependency on immediate hospital visits, and provides cost-effective preliminary dermatology support. Experimental results demonstrate high classification accuracy and reliable real-time performance.
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