This project presents a Smart Detection of Skin Diseases using Traditional diagnostic systems mostly use simple image Convolutional Neural Networks (CNN). It aims to identify various comparison methods or rule-based strategies. Although these skin conditions and suggest suitable herbal remedies. The system techniques are capable of identifying outwardly apparent uses deep learning and image processing techniques to analyze anomalies, they frequently fall short in recognizing intricate user-uploaded images and detect possible skin diseases with high accuracy. By using a CNN-based classification model trained on labeled dermatological datasets, the system can automatically don\\\'t offer recommendations for appropriate treatment that are recognize disease patterns such as acne, eczema, psoriasis, and specific to the diagnosed condition. A deep learning-based fungal infections. Once detected, it recommends appropriate system that can learn from various datasets and correctly herbal or natural treatments, offering an alternative and eco- classify diseases is needed to get around these restrictions. To preprocessing to remove noise, segmentation to extract affected regions, and feature extraction for efficient classification. A web- system should also encourage natural and affordable based interface allows users to upload images and instantly receive remedies. diagnostic results along with herbal suggestions. This solution is modular and scalable, making it easy to integrate with healthcare platforms, mobile applications, or wellness systems. By combining AI-driven diagnosis with natural remedy recommendations, the system aims to improve early detection, promote awareness of traditional medicine, and provide an affordable, accessible tool for personalized skin health management.
P et al. (Wed,) studied this question.