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Skin Disease Analysis – Dr. Advice

Author

Listed:
  • Pavithra Asst
  • Aarthi A
  • Aishwarya R
  • Divya A
  • Divyapriyadharshini S

Abstract

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.

Suggested Citation

  • Pavithra Asst & Aarthi A & Aishwarya R & Divya A & Divyapriyadharshini S, 2025. "Skin Disease Analysis – Dr. Advice," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(3), pages 01-07, June.
  • Handle: RePEc:jbh:ijsrcs:v11:y2025:i3:id:1429
    DOI: 10.32628/CSEIT24245475
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT24245475
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