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AI-Powered Dermatology: Predicting Skin Disorders Using Convolutional Neural Networks

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Last date : 27-Aug-2025

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AI-Powered Dermatology: Predicting Skin Disorders Using Convolutional Neural Networks


Mohmmad Rehan Ashraf



Mohmmad Rehan Ashraf "AI-Powered Dermatology: Predicting Skin Disorders Using Convolutional Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Advancements and Emerging Trends in Computer Applications - Innovations, Challenges, and Future Prospects, March 2025, pp.268-272, URL: https://www.ijtsrd.com/papers/ijtsrd78453.pdf

Skin conditions are a major worldwide health issue, and successful treatment depends on early detection. This study uses Microsoft's machine learning framework, ML.NET, to present a machine learning-based skin disease prediction system. In order to predict possible skin disorders, the system lets users input skin photos, which are then examined by an image classification algorithm driven by deep learning. Data pre-processing, feature extraction, model training, assessment, and deployment within an intuitive web application are all included in the methodology. The technology provides real-time categorization and analysis, producing accurate predictions instantly. Data privacy and customized access are guaranteed by secure authentication features like user registration, login, and password recovery. Effective user administration is made possible via an admin panel. This technology, which is built for speed, scalability, and dependability, helps people and medical professionals identify skin diseases in their early stages. Early diagnosis is made more accessible and effective by utilizing ML.NET's deep learning capabilities to give high-precision classification with little processing overhead. Skin disorders are a major worldwide health issue, and successful treatment depends on early detection. This paper presents a skin disease prediction system that uses Microsoft's ML.NET framework and machine learning. Users can upload photographs of their skin, which are then examined by an image classification algorithm driven by deep learning to make precise predictions about possible skin conditions. Data pre-processing, feature extraction, model training, assessment, and deployment within an intuitive web application are all included in the methodology.

Computer Vision, Medical Image Processing, ResNet50, Image Classification, Machine Learning, ML.NET, and Skin Disease Prediction


IJTSRD78453
Special Issue | Advancements and Emerging Trends in Computer Applications - Innovations, Challenges, and Future Prospects, March 2025
268-272
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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