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Skin disease diagnosis
2025-12-12
Artificial Intelligence (AI) has emerged as a transformative technology in modern healthcare, particularly in dermatology, where visual assessment is critical for disease diagnosis. This work presents DermAI, an AI-centric diagnostic system designed to classify dermatological conditions with high accuracy. The core of the system is a Convolutional Neural Network (CNN) based on MobileNetV2, optimized for mobile deployment using techniques such as quantization, pruning, and optional knowledge distillation.
Ссылка для цитирования:
ISHENBIEVA M. N. 2025. Skin disease diagnosis. PREPRINTS.RU. https://doi.org/10.24108/preprints-3114037
Список литературы
1. References
2. [1] Meir, S., Keidar, T. D., Reuveni, S., & Hirshberg, B. (2025). Optimizing Perturbations for Improved
3. Training of Machine Learning Models. arXiv:2502.04121
4. [2] Usupova, E., & Khan, A. (2025). Optimizing ML Training with Perturbed Equations. Proceedings of the
5. 6th International Conference on Problems of Cybernetics and Informatics (PCI), Baku, Azerbaijan, pp. 1-6.
6. [3] Liu, X., Qi, H., Jia, S., Guo, Y., & Liu, Y. (2025). Recent Advances in Optimization Methods for Machine
7. Learning: A Systematic Review. Mathematics, 13(13), 2210.
8. [4] Gómez-Talal, I. (2025). A Study on Efficient Perturbation-Based Erplanations. Engineering Applications
9. of Artificial Intelligence.
10. [5] AIP Publishing. (2025). Scaling of Hardware-Compatible Perturbative Training Methods for ML. APL
11. Machine Learning.
12. [6] Jeddi, A., Shafiee, M. .J., Karg, M., Scharfenberger, C., & Wong, A. (2020). Learn2Perturb: End-to-End
13. Feature Perturbation Learning to Improve Adversarial Robustness. ar Xiv:2003.01090.