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Original Article
Design and Implementation of a CNN-Based Web Application for Skin Disease Detection
G.Divya1
K.Bharathi2
T.Charulatha3
I.Harika4
G.Jothika5
1 Assistant Professor, Department of Information Technology, PSV College of Engineering and Technology, Krishnagiri, Tamil Nadu, India. 2 3 4 5 UG Scholars, Department of Information Technology, PSV College of Engineering and Technology, Krishnagiri, Tamil Nadu, India.
Published Online: January-April 2026
Pages: 413-416
Cite this article
No DOIReferences
1. Esteva, A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118.
2. Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of ICML 2019.
3. Tschandl, P., Rosendahl, C., & Kittler, H. (2018). The HAM10000 dataset: A large collection of multi-source dermatoscopic images of
common pigmented skin lesions. Scientific Data, 5, 180161.
4. Codella, N. C. F., et al. (2018). Skin Lesion Analysis Toward Melanoma Detection: ISIC 2017– 2018 Challenge Results. arXiv:1902.03368.
5. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of CVPR 2016.
6. Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556.
7. Redmon, J., et al. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of CVPR 2016.
8. Paszke, A., et al. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. NeurIPS 2019.
9. Flask Documentation. Pallets Projects. Available: https://flask.palletsprojects.com/�
10. PyTorch Official Documentation. Available: https://pytorch.org/docs/stable/index.html�
2. Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of ICML 2019.
3. Tschandl, P., Rosendahl, C., & Kittler, H. (2018). The HAM10000 dataset: A large collection of multi-source dermatoscopic images of
common pigmented skin lesions. Scientific Data, 5, 180161.
4. Codella, N. C. F., et al. (2018). Skin Lesion Analysis Toward Melanoma Detection: ISIC 2017– 2018 Challenge Results. arXiv:1902.03368.
5. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of CVPR 2016.
6. Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556.
7. Redmon, J., et al. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of CVPR 2016.
8. Paszke, A., et al. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. NeurIPS 2019.
9. Flask Documentation. Pallets Projects. Available: https://flask.palletsprojects.com/�
10. PyTorch Official Documentation. Available: https://pytorch.org/docs/stable/index.html�
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