Skin Cancer Classification with CGAN-Based Data Augmentation

Abstract: Detection of skin cancer remains a crucial medical issue because it determines the effectiveness of melanoma treatment. The current detection systems experience performance limitations because of limited labeled data which results in overfitted models that produce narrow potential outcomes while demonstrating poor generalization for unknown skin lesion classes. This research proposes solving classification challenges through the implementation of Conditional Generative Adversarial Networks which produces synthetic images that replicate the natural variability seen in real-world skin lesion scans. Synthetic images from CGANs enhance CNN training sets while improving their capabilities to identify various types of skin cancer. The proposed system exists as a platform which trains CGANs on real skin lesion datasets to produce matching synthetic imagery that amalgamates with original datasets before creating an extended CNN training set. The evaluation of proposed CNN models uses real skin lesion images with their performance evaluated through accuracy and sensitivity while measuring specificity and F1 score metrics. Model performance improves when training augmentation techniques are used instead of original image sets resulting in enhanced robustness and precision together with generalized results. Additional incorporation of synthesized data leads to substantial advancement in detecting skin conditions which dermatologists identify rarely. The research has established CGANs as promising tools for generating synthetic medical images which address data deficit challenges while showing foundationally that data augmentation strengthens deep learning model capabilities.

Skin cancer detection, Conditional Generative Adversarial Networks (CGANs), Convolutional Neural Networks (CNNs), Synthetic data augmentation, Rare lesion detection, Medical image classification, Deep learning, Data scarcity

1. Hasan, M., Barman, S. D., Islam, S., & Reza, A. W. (2019, April). Skin cancer detection using convolutional neural network. In Proceedings of the 2019 5th international conference on computing and artificial intelligence (pp. 254-258). [Google Scholar] [Crossref]

2. Brinker, T. J., Hekler, A., Utikal, J. S., Grabe, N., Schadendorf, D., Klode, J., ... & Von Kalle, C. (2018). Skin cancer classification using convolutional neural networks: systematic review. Journal of medical Internet research, 20(10), e11936. [Google Scholar] [Crossref]

3. Shah, A., Shah, M., Pandya, A., Sushra, R., Sushra, R., Mehta, M., ... & Patel, K. (2023). A comprehensive study on skin cancer detection using artificial neural network (ANN) and convolutional neural network (CNN). Clinical eHealth. [Google Scholar] [Crossref]

4. Zhang, N., Cai, Y. X., Wang, Y. Y., Tian, Y. T., Wang, X. L., & Badami, B. (2020). Skin cancer diagnosis based on optimized convolutional neural network. Artificial intelligence in medicine, 102, 101756. [Google Scholar] [Crossref]

5. Nahata, H., & Singh, S. P. (2020). Deep learning solutions for skin cancer detection and diagnosis. Machine learning with health care perspective: machine learning and healthcare, 159- 182. [Google Scholar] [Crossref]

6. Malo, D. C., Rahman, M. M., Mahbub, J., & Khan, M. M. (2022, January). Skin cancer detection using convolutional neural network. In 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0169- 0176). IEEE. [Google Scholar] [Crossref]

7. Han, S. S., Moon, I. J., Lim, W., Suh, I. S., Lee, S. Y., Na, J. I., ... & Chang, S. E. (2020). Keratinocytic skin cancer detection on the face using region-based convolutional neural network. JAMA dermatology, 156(1), 29-37. [Google Scholar] [Crossref]

8. Rezaoana, N., Hossain, M. S., & Andersson, K. (2020, December). Detection and classification of skin cancer by using a parallel CNN model. In 2020 IEEE international women in engineering (WIE) conference on electrical and computer engineering (WIECON- ECE) (pp. 380-386). IEEE. [Google Scholar] [Crossref]

9. Haggenmüller, S., Maron, R. C., Hekler, A., Utikal, J. S., Barata, C., Barnhill, R. L., ... & Brinker, T. J. (2021). Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts. European Journal of Cancer, 156, 202-216. [Google Scholar] [Crossref]

10. Junayed, M. S., Anjum, N., Noman, A., & Islam, B. (2021). A deep CNN model for skin cancer detection and classification. [Google Scholar] [Crossref]

11. Thurnhofer-Hemsi, K., & Domínguez, E. (2021). A convolutional neural network framework for accurate skin cancer detection. Neural Processing Letters, 53(5), 3073-3093. [Google Scholar] [Crossref]

12. Aima, A., & Sharma, A. K. (2019, February). Predictive approach for melanoma skin Cancer detection using CNN. In Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur-India. [Google Scholar] [Crossref]

13. Garg, R., Maheshwari, S., & Shukla, A. (2021). Decision support system for detection and classification of skin cancer using CNN. In Innovations in computational intelligence and computer vision: proceedings of ICICV 2020 (pp. 578-586). Springer Singapore. [Google Scholar] [Crossref]

14. Fu’adah, Y. N., Pratiwi, N. C., Pramudito, M. A., & Ibrahim,N. (2020, December). Convolutional neural network (CNN) for automatic skin cancer classification system. In IOP conference series: materials science and engineering (Vol. 982, No. 1, p. 012005). IOP Publishing. [Google Scholar] [Crossref]

15. Dorj, U. O., Lee, K. K., Choi, J. Y., & Lee, M. (2018). The skin cancer classification using deep convolutional neural network. Multimedia Tools and Applications, 77, 9909-9924. [Google Scholar] [Crossref]

16. Rajarajeswari, S., Prassanna, J., Quadir, M. A., Jackson, J. C., Sharma, S., & Rajesh, B. (2022). Skin cancer detection using deep learning. Research Journal of Pharmacy and Technology, 15(10), 4519- 4525. [Google Scholar] [Crossref]

17. Hasan, M. R., Fatemi, M. I., Monirujjaman Khan, M., Kaur, M., & Zaguia, A. (2021). Comparative analysis of skin cancer (benign vs. malignant) detection using convolutional neural networks. Journal of Healthcare Engineering, 2021(1), 5895156. [Google Scholar] [Crossref]

18. Gouda, W., Sama, N. U., Al-Waakid, G., Humayun, M., & Jhanjhi, N. Z. (2022, June). Detection of skin cancer based on skin lesion images using deep learning. In Healthcare (Vol. 10, No. 7, p. 1183). MDPI. [Google Scholar] [Crossref]

19. Saba, T., Khan, M. A., Rehman, A., & Marie-Sainte, S. L. (2019). Region extraction and classification of skin cancer: A heterogeneous framework of deep CNN features fusion and reduction. Journal of medical systems, 43(9), 289. [Google Scholar] [Crossref]

20. Tschandl, P., Rosendahl, C., Akay, B. N., Argenziano, G., Blum, A., Braun, R. P., ... & Kittler, H. (2019). Expert-level diagnosis of nonpigmented skin cancer by combined convolutional neural networks. JAMA dermatology, 155(1), 58-65. [Google Scholar] [Crossref]

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