Published on: September 2026
AI-BASED SKIN DISEASE DETECTION AND CLASSIFICATION USING DEEP LEARNING
Pranali Bhanarkar Chhakuli Naktode Pranjali Nagose Trupti Navghare
Dr. Sushama Telrandhe
Article Status
Available Documents
Abstract
Keywords: Convolutional Neural Networks (CNN), Deep Learning, Skin Disease Detection, EfficientNetB0, HAM10000, Transfer Learning, Image Preprocessing, TensorFlow Lite, Android Application
How to Cite this Paper
Bhanarkar, P., Naktode, C., Nagose, P. & Navghare, T. (2026). AI-Based Skin Disease Detection and Classification Using Deep Learning. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(9), 1-9. https://doi.org/10.55041/ijcope.v2i9.002
Bhanarkar, Pranali, et al.. "AI-Based Skin Disease Detection and Classification Using Deep Learning." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 9, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i9.002.
Bhanarkar, Pranali,Chhakuli Naktode,Pranjali Nagose, and Trupti Navghare. "AI-Based Skin Disease Detection and Classification Using Deep Learning." International Journal of Creative and Open Research in Engineering and Management 02, no. 9 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i9.002.
References
- Tschandl, C. Rosendahl, and H. Kittler, “The HAM10000 Dataset: A Large Collection of Multi-Source Dermatoscopic Images of Common Pigmented Skin Lesions,” Scientific Data, vol. 5, 2018.
- C. F. Codella et al., “Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI),” IEEE International Symposium on Biomedical Imaging (ISBI).
- Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” in Proceedings of the 36th International Conference on Machine Learning (ICML), 2019.
- He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
- Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
- V. Telrandhe, “AI-Based Skin Disease Detection and Classification Using Deep Learning,” International Journal of Computer Information Systems and Industrial Management
Applications, vol. 18, no. 3s, pp. 261–272, 2026, doi: 10.70917/ijcisim-2026-2328.
- Venu Gopal, A. S. H. N. Pavan, K. Nagendra, M. Pavan Sai, and A. Vijay Kumar, “Skin Disease Detection Using Deep Learning Techniques,” Journal of Prevention, Diagnosis and Management of Human Diseases, vol. 4, no. 1, pp. 40–49, 2024, doi: 10.55529/jpdmhd.41.40.49.
- Ahmad, M. Saleem, J. A. Malik, W. A. Bukhari, M. I. Kashif, H. Salahuddin, M. A. U. Rehman, and A. U. Rehman, “Mobile Application for Skin Disease Classification Using CNN with User Privacy,” Journal of Computing & Biomedical Informatics, 2024.
- Rezaee and H. Ghayoumi Zadeh, “Self-attention Transformer Unit-based Deep Learning Framework for Skin Lesions Classification in Smart Healthcare,” Discover Applied Sciences, vol. 6, article 3, 2024.
- P. Yadav, B. Sharma, S. Chauhan, J. L. Webber, and
- Mehbodniya, “Dual Scale Lightweight Cross Attention Transformer for Skin Lesion Classification,” PLOS ONE, vol. 19, 2024, doi: 10.1371/journal.pone.0312598.
Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Sep 03 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

