VGG Convolutional Neural Network Classification of Hyperspectral Images of Skin Neoplasms

Boris V. Grechkin
Samara National Research University, Russia

Vseslav O. Vinikurov
Samara National Research University, Russia

Yulia A. Khristoforova
Samara National Research University, Russia

Irina A. Matveeva (Login required)
Samara National Research University, Russia

Paper #8964 received 28 Apr 2023; revised manuscript received 23 Sep 2022; accepted for publication 24 Sep 2023; published online 11 Nov 2023.

DOI: 10.18287/JBPE23.09.040304


The article is devoted to the problem of early diagnosis of cancer. In last five years, various optical methods have been increasingly used to study biological tissues. This study aims to investigate the capability of a convolutional neural network classifier to diagnose skin cancers. The article analyzes hyperspectral images of malignant melanoma and pigmented nevus. A hyperspectral image classifier based on a deep learning neural network was developed. The results show a classification accuracy of diagnose prediction (on test data) at the level of 95%, which demonstrates the possibility of using machine learning for the classification of hyperspectral images of skin diseases. The results of the study can be applied in medical decision-making systems.


hyperspectral imaging; malignant melanoma; classification; medical diagnostics; neural network; pigmented nevus; skin cancer

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1. “Cancer,” World Health Organization, 3 February 2022 (accessed 1 November 2022). [].

2. J. Ferlay, M. Colombet, I. Soerjomataram, D. M. Parkin, M. Piñeros, A. Znaor, and F. Bray, “Cancer statistics for the year 2020: An overview,” International Journal of Cancer 149(4), 778–789 (2021).

3. V. O. Vinokurov, I. A. Matveeva, Y. A. Khristoforova, O. O. Myakinin, I. A. Bratchenko, L. A. Bratchenko, A. A. Moryatov, S. G. Kozlov, A. S. Machikhin, I. Abdulhalim, and V. P. Zakharov, “Neural network classifier of hyperspectral images of skin pathologies,” Computer Optics 45(6), 879–886 (2021).

4. S. Karim, A. Qadir, U. Farooq, M. Shakir, and A. A. Laghari, “Hyperspectral imaging: a review and trends towards medical imaging,” Current Medical Imaging 19(5), 417–427 (2022).

5. C. I. Chang, “Hyperspectral imaging: techniques for spectral detection and classification,” Springer New York, NY (2003).

6. G. Lu, B. Fei, “Medical hyperspectral imaging: A review,” Journal of Biomedical Optics 19(1), 10901 (2014).

7. V. P. Sherendak, I. A. Bratchenko, O. O. Myakinin, P. N. Volkhin, Y. A. Khristoforova, A. A. Moryatov, A. S. Machikhin, P. E. Pozhar, S. V. Kozlov, and V. P. Zakharov, “Hyperspectral in vivo analysis of normal skin chromophores and visualization of oncological pathologies,” Computer Optics 43(4), 661–670 (2019).

8. H. Rashid, M. A. Tanveer, and H. A. Khan, “Skin lesion classification using GAN based data augmentation,” Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 916–919 (2019).

9. D. Bisla, A. Choromanska, J. A. Stein, D. Polsky, and R. Berman, “Towards automated melanoma detection with deep learning: data purification and augmentation,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2720–2728 (2019).

10. L. Yu, H. Chen, Q. Dou, J. Qin, and P. A. Heng, “Automated melanoma recognition in dermoscopy images via very deep residual networks,” IEEE Transactions on Medical Imaging 36(4), 994–1004 (2017).

11. T. DeVries, D. Ramachandram, “Skin lesion classification using deep multi-scale convolutional neural networks,” arXiv Preprint arXiv:1703.01402 (2017).

12. M. Dildar, S. Akram, M. Irfan, H. U. Khan, M. Ramzan, A. R. Mahmood, S. A. Alsaiari, A. H. M. Saeed, M. O. Alraddadi, and M. H. Mahnashi, “Skin cancer detection: a review using deep learning techniques,” International Journal of Environmental Research and Public Health 18(10), 5479 (2021)

13. A. Machikhin, V. Batshev, and V. Pozhar, “Aberration analysis of AOTF-based spectral imaging systems,” Journal of the Optical Society of America A 34(7), 1109–1113 (2017).

14. K. Simonyan, A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv Preprint arXiv:1409.1556 (2014).

15. L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaría, M. A. Fadhel, M. Al-Amidie, and L. Farhan, “Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions,” Journal of Big Data 8, 53 (2021).

16. I. A. Bratchenko, L. A. Bratchenko, A. A. Moryatov, Y. A. Khristoforova, D. N. Artemyev, O. O. Myakinin, A. E. Orlov, S. V. Kozlov, and V. P. Zakharov, “In vivo diagnosis of skin cancer with a portable Raman spectroscopic device,” Experimental Dermatology 30(5), 652–663 (2021).

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