SkinSense: A Smart Chatbot for Psoriasis Detection Using Machine Learning and Conversational AI

Abstract—“SkinSense: Smart Chatbot for Psoriasis Detection” aims to tackle the challenges of early psoriasis detection and patient support by integrating machine learning with conversational AI. Psoriasis, a chronic inflammatory skin condition affecting millions globally, often remains undiagnosed due to stigma and hesitation to seek medical help. This research introduces a chatbot that utilizes a trained MobileNet V2 model, enhanced with Contrast Limited Adaptive Histogram Equalization (CLAHE), to analyze user-uploaded images for psoriasis detection. Designed with a friendly and empathetic interface, the chatbot delivers educational content and motivates users to consult healthcare professionals. Preliminary model evaluation reveals promising accuracy in psoriasis detection on a limited test set, while user feedback underscores the chatbot’s success in reducing stigma and encouraging treatment-seeking behavior. This work advances dermatological care by merging cutting-edge image analysis with supportive technology, providing a scalable solution for early detection and patient engagement. Index Terms—Psoriasis Detection, Machine Learning, Conversational AI, MobileNet V2, Chatbot, Dermatological Care, Image Analysis, Stigma Reduction.