Publication

Abstract : Skin diseases, such as monkeypox, chickenpox, ringworm, vasculitis, and warts molluscum, are a serious global health issue, impacting millions of people around the world. Early and proper diagnosis is critical to provide timely medical care and avoid complications. In this paper, we suggest an advanced system for skin disease detection using InceptionNet v3, an advanced deep learning model, for efficient classification. The database consists of six classes of skin diseases, having a total of 6000 images to promote variability and aid model generalization. The accuracy of the model was 94 %, proving that it is effective in detecting dermatological conditions. In addition, the trained model was implemented using opencv, allowing on-device, real-time detection, making it portable for remote and resource-constrained regions. The combination of deep learning and edge computing presents a cost-effective, efficient, and scalable means of automated skin disease diagnosis, enabling future AI-driven revolution in medical imaging and teledermatology.