Skin Disease Smart Monitor: Using AI Precision

Abstract

The Skin diseases, including life-   threatening   conditions like melanoma, are a significant global health concern.  Early and  accurate diagnosis is critical for improving patient       outcomes. Traditional dermatological methods, ,    such as clinical examination and histopathological analysis, are often time-consuming and require expert interpretation. In recent years, deep learning-based approaches, particularly Convolutional Neural Networks (CNNs), have shown promising results in automated skin disease detection. This research focuses on developing a CNN-based model for skin lesion classification, leveraging the HAM10000  dataset, health applications and telemedicine platforms could enable real-time, accessible, and  cost-effective diagnosis, particularly in remote and underserved regions. Future research will focus on expanding dataset diversity, improving model robustness, and integrating multimodal AI   approaches for enhanced predictive  accuracy.