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Original Article
Dermanex: Next Generation Ai Skin Disease Detection Using Deep Learning & Machine Learning
Sudheer Kumar Kolahala1
Venkat Sai Keesara2
Tejaswini Avula3
Dr. Srinivas Jagirdar4
1 2 3 UG Scholar, Department of Computer Engineering, Matrusri Engineering College, Hyderabad, Telangana, India. 4 Associate Professor & HOD, Department of Information Technology, Matrusri Engineering College, Hyderabad, Telangana, India.
Published Online: January-April 2026
Pages: 307-312
Cite this article
No DOIReferences
1. R. Hay et al., "The global burden of skin disease in 2010," Journal of Investigative Dermatology, vol. 134, no. 6, pp. 1527–1534, 2014.
2. American Cancer Society, "Cancer Facts & Figures 2023," Atlanta, GA, USA: American Cancer Society, 2023.
3. A. Esteva et al., "Dermatologist-level classification of skin cancer with deep neural networks," Nature, vol. 542, no. 7639, pp. 115–118,
2017.
4. N. C. F. Codella et al., "Skin lesion analysis toward melanoma detection: A challenge at the 2017 ISBI," in Proc. IEEE 15th Int. Symp.
Biomed. Imaging (ISBI), 2018, pp. 168–172.
5. M. Tan and Q. Le, "EfficientNet: Rethinking model scaling for convolutional neural networks," in Proc. Int. Conf. Mach. Learn. (ICML),
2019, pp. 6105–6114.
6. Y. Liu et al., "A deep learning system for differential diagnosis of skin diseases," Nature Medicine, vol. 26, no. 6, pp. 900–908, 2020.
7. J. Hu, L. Shen, and G. Sun, "Squeeze-and-excitation networks," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2018, pp.
7132–7141.
8. S. Woo et al., "CBAM: Convolutional block attention module," in Proc. Eur. Conf. Comput. Vis. (ECCV), 2018, pp. 3–19.
9. R. R. Selvaraju et al., "Grad-CAM: Visual explanations from deep networks via gradient-based localization," in Proc. IEEE Int. Conf.
Comput. Vis. (ICCV), 2017, pp. 618–626.
10. H. A. Haenssle et al., "Man against machine: Diagnostic performance of a deep learning CNN for dermoscopic melanoma recognition,"
Annals of Oncology, vol. 29, no. 8, pp. 1836–1842, 2018.
11. P. Tschandl et al., "The HAM10000 dataset: A large collection of multi-source dermatoscopic images of common pigmented skin lesions,"
Scientific Data, vol. 5, no. 1, pp. 1–9, 2018.
12. V. Rotemberg et al., "A patient-centric dataset and challenge proposal for identifying melanomas using clinical context," Scientific Data,
vol. 8, no. 1, pp. 1–8, 2021.
13. T.-Y. Lin et al., "Focal loss for dense object detection," in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 2017, pp. 2980–2988.
14. Y. Cui et al., "Class-balanced loss based on effective number of samples," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR),
2019, pp. 9268–9277.
15. K. He et al., "Deep residual learning for image recognition," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016, pp. 770–
778.
16. A. G. Howard et al., "MobileNets: Efficient convolutional neural networks for mobile vision applications," arXiv preprint arXiv:1704.04861,
2017.
2. American Cancer Society, "Cancer Facts & Figures 2023," Atlanta, GA, USA: American Cancer Society, 2023.
3. A. Esteva et al., "Dermatologist-level classification of skin cancer with deep neural networks," Nature, vol. 542, no. 7639, pp. 115–118,
2017.
4. N. C. F. Codella et al., "Skin lesion analysis toward melanoma detection: A challenge at the 2017 ISBI," in Proc. IEEE 15th Int. Symp.
Biomed. Imaging (ISBI), 2018, pp. 168–172.
5. M. Tan and Q. Le, "EfficientNet: Rethinking model scaling for convolutional neural networks," in Proc. Int. Conf. Mach. Learn. (ICML),
2019, pp. 6105–6114.
6. Y. Liu et al., "A deep learning system for differential diagnosis of skin diseases," Nature Medicine, vol. 26, no. 6, pp. 900–908, 2020.
7. J. Hu, L. Shen, and G. Sun, "Squeeze-and-excitation networks," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2018, pp.
7132–7141.
8. S. Woo et al., "CBAM: Convolutional block attention module," in Proc. Eur. Conf. Comput. Vis. (ECCV), 2018, pp. 3–19.
9. R. R. Selvaraju et al., "Grad-CAM: Visual explanations from deep networks via gradient-based localization," in Proc. IEEE Int. Conf.
Comput. Vis. (ICCV), 2017, pp. 618–626.
10. H. A. Haenssle et al., "Man against machine: Diagnostic performance of a deep learning CNN for dermoscopic melanoma recognition,"
Annals of Oncology, vol. 29, no. 8, pp. 1836–1842, 2018.
11. P. Tschandl et al., "The HAM10000 dataset: A large collection of multi-source dermatoscopic images of common pigmented skin lesions,"
Scientific Data, vol. 5, no. 1, pp. 1–9, 2018.
12. V. Rotemberg et al., "A patient-centric dataset and challenge proposal for identifying melanomas using clinical context," Scientific Data,
vol. 8, no. 1, pp. 1–8, 2021.
13. T.-Y. Lin et al., "Focal loss for dense object detection," in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 2017, pp. 2980–2988.
14. Y. Cui et al., "Class-balanced loss based on effective number of samples," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR),
2019, pp. 9268–9277.
15. K. He et al., "Deep residual learning for image recognition," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016, pp. 770–
778.
16. A. G. Howard et al., "MobileNets: Efficient convolutional neural networks for mobile vision applications," arXiv preprint arXiv:1704.04861,
2017.
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