Skin Disease Analysis – Dr. Advice
DOI:
https://doi.org/10.32628/IJSRCSEITKeywords:
Skin Disease Detection, Artificial Intelligence (AI), Machine Learning (ML) Deep Learning, Computer-Aided Diagnosis (CAD), Android Application, Real-Time Processing, Mobile Accessibility, Image Processing, User-Friendly Interface, Data Security, Neural NeAbstract
Traditional skin disease diagnosis is often slow, expensive, and requires in-person consultations, making it less accessible for many individuals. Conventional Computer-Aided Diagnosis (CAD) methods rely on manually extracted features like color, texture, and shape, which limits their accuracy, particularly across diverse skin tones. Additionally, online symptom checkers and existing AI models often lack real-time processing capabilities and mobile accessibility, reducing their effectiveness in providing instant and accurate results. To address these limitations, we developed Dr.Advice, an AI-powered Android application designed for real-time skin disease detection. Built with Python, Java, C++, Kotlin, and Android Studio SDK, Dr.Advice integrates advanced machine learning techniques to analyze skin images, detect conditions, and provide diagnostic insights. The application features real-time image processing, a user-friendly interface, and secure data handling, ensuring privacy and accuracy. Trained on diverse datasets, the model enhances detection accuracy across various skin tones. By offering fast, reliable, and accessible early diagnosis, Dr.Advice aims to revolutionize dermatological care, improving treatment outcomes and making skin disease detection more efficient and widely available.
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