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May 18, 20260 citationsOpen Access

SkinSense: A Skin Disease Detection System

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AJAkshada Ganesh JadhavAKAvdhoot Vijay KodeSGSakshi Mahesh Gore

Key Points

  • The aim is to develop a system that uses AI to detect skin diseases from images, facilitating early treatment.
  • Implemented an AI-based detection system analyzing skin images.
  • Employed a deep learning image classification model for prediction.
  • Provided features like preventive guidance and chatbot assistance.
  • Generated predicted disease results with associated confidence scores.
  • Improved accessibility to dermatological assessments.
  • Promoted early management of skin-related health issues.

Abstract

Skin diseases have become increasingly common due to environmental changes, lifestyle factors, and limited availability of dermatological services. Early identification of skin disorders is essential to prevent complications and to support timely treatment. This paper presents the implementation of an artificial intelligence-based skin disease detection and management system that analyzes skin images to assist users in identifying possible skin conditions. The system enables users to upload or capture images of affected skin areas and optionally provide visible symptom information to improve prediction reliability. A deep learning–based image classification model is employed to process the input images and generate predicted disease results along with confidence scores. In addition to disease detection, the platform provides preventive guidance, basic care recommendations, automated report generation, and healthcare support features such as chatbot assistance and doctor appointment facilitation. The implemented system aims to improve accessibility to preliminary dermatological assessment, reduce dependence on immediate physical consultations, and promote early awareness and management of skin-related health issues.

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Cite This Study

Jadhav et al. (2026) studied this question.

synapsesocial.com/papers/6a0aad5c5ba8ef6d83b70ce2https://doi.org/10.5281/zenodo.20226219
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