| Issue |
EPJ Web Conf.
Volume 341, 2025
2nd International Conference on Advent Trends in Computational Intelligence and Communication Technologies (ICATCICT 2025)
|
|
|---|---|---|
| Article Number | 01017 | |
| Number of page(s) | 11 | |
| DOI | https://doi.org/10.1051/epjconf/202534101017 | |
| Published online | 20 November 2025 | |
https://doi.org/10.1051/epjconf/202534101017
Image-based skin disease detection: Psoriasis, Eczema, and Acne using VGG16 and ResNet-50
Department of CSE(AIML) , Kolhapur Institute of Technology’s College of Engineering, Kolhapur, Maharashtra, India
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Published online: 20 November 2025
Millions of people around the world are affected by skin diseases, which represent a serious health risk. For these illnesses to be effectively treated, a timely and precise diagnosis is essential. This project uses deep learning techniques, specifically the VGG16 architecture and Resnet50 architecture, to provide a reliable solution for the categorization of skin diseases. The creation of an extremely precise and effective model for the automated classification of skin conditions is the main goal of this study. Three different classifications of skin conditions, including psoriasis, eczema, and acne, make up the dataset used in this project. The models are adaptable and capable of managing a variety of dermatological issues because each class in the dataset has been carefully chosen to reflect a broad range of skin disorders. The known convolution neural network (CNN) models called the VGG16 architecture and Resnet50 architecture are used because of their exceptional feature extraction capabilities. Move the pre-trained VGG16 model is refined using learning on the dataset of skin diseases. To guarantee the reliability of the models, a thorough cross-validation procedure is used for training, validation, and testing.
Key words: Image Classification / VGG16 / Resnet50 / Transfer Learning / Convolutional Neural Network(CNN) / Tensorflow
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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