Abstract
Melanins and sebums are two important criteria for the quality evaluation of skin, and they are capable of providing customized suggestions for skin care. Currently, their detection is heavily relied on manual process performed by specialists in laboratory. Although efficient, such a manual detection is an expensive and labor-intensive procedure, and hence, there has been great interest in developing computational models for automatic detection of melanins and sebums from skin images. In this work, we propose an automatic detection algorithm, namely DAME, to identify these two kinds of substances based on a generative adversarial network (GAN). To do so, DAME makes use of a variant of GAN, i.e., pix2pix, due to its strength in image generation by learning the structural and contextual information of melanins and sebums observed from skin images. With these additional augmented images, a robust U-Net model can be learned for automatically detecting and marking melanins and sebums. To evaluate the performance of DAME, we have conducted a series of experiments by comparing it with several existing algorithms on real image datasets, and the results have demonstrated that DAME yields a substantially better detection accuracy than previously published algorithms in terms of several independent evaluation metrics. Moreover, DAME is believed to be more robust than other algorithms, as it obtains the smallest variance for each metric. Hence, DAME makes it possible to automatically detect melanins and sebums with a promising performance. Due to the strong learning ability of GAN, DAME is also able to identify melanins and sebums that are possibly ignored by specialists. The source codes of DAME and datasets used in the experiments are available at https://github.com/reBioco-der/DAME.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Razzak MI, Naz S, Zaib A. Deep learning for medical image processing: Overview, challenges and the future. In: Classification in BioApps. Springer, 2018. pp 323–350.
Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172(5):1122–31.
Wheeler MH, Bell AA. Melanins and their importance in pathogenic fungi. In: Current topics in medical mycology, Springer, 1988. pp 338–387.
Draelos ZD. Degradation and migration of facial foundations. J Am Acad Dermatol. 2001;45(4):542–3.
Hu L, Yuan X, Liu X, Xiong S, Luo X. Efficiently detecting protein complexes from protein interaction networks via alternating direction method of multipliers. IEEE/ACM Trans Comput Biol Bioinform. 2018;16(6):1922–35.
Hu L, Zhang J, Pan X, Yan H, You ZH. Hiscf: leveraging higher-order structures for clustering analysis in biological networks. Bioinformatics. 2020.
Hu L, Chan KC, Yuan X, Xiong S. A variational bayesian framework for cluster analysis in a complex network. IEEE Trans Knowl Data Eng. 2020;32(11):2115–28.
Hu L, Yang S, Luo X, Zhou M. An algorithm of inductively identifying clusters from attributed graphs. IEEE Transactions on Big Data. 2020.
Hashimoto T, Yamashita K, Yamazaki K, Hirayama K, Yabuzaki J, Kobayashi H. Study of analysis and quantitative estimation of melanin in face epidermal corneocyte. Transactions of the Japan Society of Mechanical Engineers, Part C. 2012;78(786):508–22.
Damian FA, Moldovanu S, Dey N, Ashour AS, Moraru L. Feature selection of non-dermoscopic skin lesion images for nevus and melanoma classification. Computation. 2020;8(2):41.
Oliveira RB, Pereira AS, Tavares JMR. Computational diagnosis of skin lesions from dermoscopic images using combined features. Neural Comput Applic. 2019;31(10):6091–111.
Isola P, Zhu JY, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. pp 1125–1134.
Schmidt P, Gaser C, Arsic M, Buck D, Förschler A, Berthele A, Hoshi M, Ilg R, Schmid VJ, Zimmer C, et al. An automated tool for detection of flair-hyperintense white-matter lesions in multiple sclerosis. Neuroimage. 2012;59(4):3774–83.
Rahman MM, Bhattacharya P, Desai BC. A multiple expert-based melanoma recognition system for dermoscopic images of pigmented skin lesions. In: 2008 8th IEEE International Conference on BioInformatics and BioEngineering. IEEE, 2008. pp 1–6.
Sarraf S, Tofighi G. Deep learning-based pipeline to recognize alzheimer’s disease using fmri data. In: 2016 Future Technologies Conference (FTC). IEEE, 2016. pp 816–820.
Li F, Tran L, Thung KH, Ji S, Shen D, Li J. A robust deep model for improved classification of ad/mci patients. IEEE J Biomed Health Inform. 2015;19(5):1610–6.
Sirinukunwattana K, Raza SEA, Tsang YW, Snead DR, Cree IA, Rajpoot NM. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging. 2016;35(5):1196–206.
Cruz-Roa AA, Ovalle JEA, Madabhushi A, Osorio FAG. A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer 2013. pp 403–410.
Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, Van Riel SJ, Wille MMW, Naqibullah M, Sánchez CI, van Ginneken B. Pulmonary nodule detection in ct images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging. 2016;35(5):1160–9.
Dey N. Uneven illumination correction of digital images: a survey of the state-of-the-art. Optik. 2019;183:483–95.
Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer, 2015. pp 234–241.
Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv preprint 2014. arXiv:14126980.
Zhu JY, Park T, Isola P, Efros AA. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision. 2017. pp 2223–2232.
Funding
This study was funded by the Pioneer Hundred Talents Program of Chinese Academy of Sciences.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Ethical Approval
This article does not contain any studies with human participants performed by any of the authors.
Conflicts of Interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Rights and permissions
About this article
Cite this article
Hu, L., Chen, Q., Qiao, L. et al. Automatic Detection of Melanins and Sebums from Skin Images Using a Generative Adversarial Network. Cogn Comput 14, 1599–1608 (2022). https://doi.org/10.1007/s12559-021-09870-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12559-021-09870-5