SkinSense: A Skin Disease Detection System

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.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-16
DOI
https://doi.org/10.5281/zenodo.20226218
Primary Topic
Cutaneous Melanoma Detection and Management
Type
article
Field-Weighted Citation Impact
0.00
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article

SkinSense: A Skin Disease Detection System

Akshada Ganesh Jadhav, Avdhoot Vijay Kode, Narendra Nandu Ghuge, Sakshi Mahesh Gore et al.
Zenodo (CERN European Organization for Nuclear Research)
Cutaneous Melanoma Detection and Management
article

SkinSense: A Skin Disease Detection System

Akshada Ganesh Jadhav, Avdhoot Vijay Kode, Narendra Nandu Ghuge, Sakshi Mahesh Gore, S.D.Aher
article en

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.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 6%
Cutaneous Melanoma Detection and Management
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SkinSense: A Skin Disease Detection System — Akshada Ganesh Jadhav, Avdhoot Vijay Kode, et al. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS