Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
권호 표지
DOI QR코드

DOI QR Code

An Experimental Comparison of CNN-based Deep Learning Algorithms for Recognition of Beauty-related Skin Disease

  • Bae, Chang-Hui (Dept. of Aeronautical Software Engineering, Kyungwoon University) ;
  • Cho, Won-Young (Dept. of Aeronautical Software Engineering, Kyungwoon University) ;
  • Kim, Hyeong-Jun (Dept. of Aeronautical Software Engineering, Kyungwoon University) ;
  • Ha, Ok-Kyoon (Dept. of Aeronautical Software Engineering, Kyungwoon University)
  • Received : 2020.10.14
  • Accepted : 2020.11.19
  • Published : 2020.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we empirically compare the effectiveness of training models to recognize beauty-related skin disease using supervised deep learning algorithms. Recently, deep learning algorithms are being actively applied for various fields such as industry, education, and medical. For instance, in the medical field, the ability to diagnose cutaneous cancer using deep learning based artificial intelligence has improved to the experts level. However, there are still insufficient cases applied to disease related to skin beauty. This study experimentally compares the effectiveness of identifying beauty-related skin disease by applying deep learning algorithms, considering CNN, ResNet, and SE-ResNet. The experimental results using these training models show that the accuracy of CNN is 71.5% on average, ResNet is 90.6% on average, and SE-ResNet is 95.3% on average. In particular, the SE-ResNet-50 model, which is a SE-ResNet algorithm with 50 hierarchical structures, showed the most effective result for identifying beauty-related skin diseases with an average accuracy of 96.2%. The purpose of this paper is to study effective training and methods of deep learning algorithms in consideration of the identification for beauty-related skin disease. Thus, it will be able to contribute to the development of services used to treat and easy the skin disease.

본 논문에서는 딥러닝 지도학습 알고리즘을 사용한 학습 모델을 대상으로 미용 관련 피부질환 인식의 효과성을 실험적으로 비교한다. 최근 딥러닝 기술을 산업, 교육, 의료 등 다양한 분야에 적용하고 있으며, 의료 분야에서는 중요 피부질환 중 하나인 피부암 식별의 수준을 전문가 수준으로 높인 성과를 보이고 있다. 그러나 아직 피부미용과 관련된 질환에 적용한 사례가 다양하지 못하다. 따라서 딥러닝 기반 이미지 분류에 활용도가 높은 CNN 알고리즘을 비롯하여 ResNet, SE-ResNet을 적용하여 실험적으로 정확도를 비교함으로써 미용 관련 피부질환을 판단하는 효과성을 평가한다. 각 알고리즘을 적용한 학습 모델을 실험한 결과에서 CNN의 경우 평균 71.5%, ResNet은 평균 90.6%, SE-ResNet은 평균 95.3%의 정확도를 보였다. 특히 학습 깊이를 다르게하여 비교한 결과 50개의 계층 구조를 갖는 SE-ResNet-50 모델이 평균 96.2%의 정확도로 미용 관련 피부질환 식별을 위해 가장 효과적인 결과를 보였다. 본 논문의 목적은 피부 미용과 관련된 질환의 판별을 고려하여 효과적인 딥러닝 알고리즘의 학습과 방법을 연구하기 위한 것으로 이를 통해 미용 관련 피부질환 개선을 위한 서비스 개발로 확장할 수 있을 것이다.

Keywords

References

  1. Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, Vol. 521, No. 7553, pp. 436-444, May, 2015. DOI: 10.1038/nature14539.
  2. G. Litjens, T. Kooi, B. E. Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian, J. Van der Laak, B. Van Ginnekenn and C. I. Sanchez, "A Survey on Deep Learning in Medical Image Analysis," Medical Image Analysis, Vol. 42, pp. 60-88, December, 201. DOI: 10.1016/j.media.2017.07.005.
  3. M. N. Bajwa, M. I. Malik, S. A. Siddiqui, A. Dengel, F. Shafait, W. Neumeier, and S. Ahmed, "Two-stage Framework for Optic Disc Localization and Glaucoma Classification in Retinal Fundus Images using Deep Learning," BMC Medical Informatics and Decision Making, Vol. 19, Article No. 136, July, 2019. DOI: 10.1186/s12911-019-0842-8.
  4. P. Teare, M. Fishman, O. Benzaquen, E. Toledano, and E. Elnekave, "Malignancy Detection on Mammography Using Dual Deep Convolutional Neural Networks and Genetically Discovered False Color Input Enhancement," Jounal of Digital Imaging, Vol. 30, No. 4, pp. 499-505, August, 2017. DOI: 10.1007/s10278-017-9993-2.
  5. M. N. Bajwa, Y. Taniguchi, M. I. Malik, W. Neumeier, A. Dengel, S. Ahmed, Y. Zheng, B. M. Willams, and K. Chen, "Combining Fine-and Coarse-Grained Classifiers for Diabetic Retinopathy Detection," In Proceedings of the Annual Conference on Medical Image Understanding and Analysis, CCIS, Vol. 1065, pp. 242-253, July 2019. DOI: 10.1007/978-3-030-39343-4_21
  6. M. Abedini, N. C. F. Codella, J. H. Connell, R. Garnavi, M. Merler, S. Pankanti, J. R. Smith, and T. Syeda-Mahmood, "A generalized framework for medical image classification and recognition," IBM Journal of Research and Development, Vol. 59, No. 2/3, pp. 1:1-1:18, March, 2015. DOI: 10.1147/JRD.2015.2390017.
  7. C.-C. Jay Kuo, "Understanding Convolutional Neural Networks with Mathematical Model," Journal of Visual Communication and Image Representation, Vol. 41, pp. 406-413, November, 2016. DOI: 10.1016/j.jvcir.2016.11.003.
  8. K. He, X. Zhang, S. Ren, and J. Sun., "Deep Residual Learning for Image Recognition," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp. 770-778, IEEE, June, 2016. DOI: 10.1109/CVPR.2016.90.
  9. J. Hu, L. Shen, and G. Sun, "Squeeze-and-Excitation Networks", in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018, IEEE, pp. 7132-7141, June, 2018. DOI: 10.1109/CVPR.2018.00745.
  10. A. Esteva, B. Kuprel, R. A. Novoa, S. M. Swetter, H. M. Blau, and S. Thrun, "Dermatologist-level Classification of Skin Cancer with Deep Neural Networks," Nature, Vol. 542, pp. 115-118, February, 2017. DOI: 10.1038/nature21056.
  11. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv: 1409.1556.
  12. U. Dorj, K. Lee, J. Choi, and M. Lee, "The Skin Cancer Classification using Deep Convolutional Neural Network," Multimed Tools and Applications, Vol. 77, Issue 8, pp. 9909-9924, Springer, February, 2018. DOI: 10.1007/s11042-018-5714-1.
  13. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Communications of the ACM, Vol. 60, No. 6, ACM, May 2017. DOI: 10.1145/3065386.
  14. P. Tschandl, C. Rosendahl, B. N. Akay, et al., "Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks," JAMA Dermatol, Vol.155, No. 1, pp. 58-65, American Medical Association, Jan. 2019. DOI: 10.1001/jamadermatol.2018.4378.
  15. J. S. Alarifi, M. Goyal, A. K. Davison, D. Dancey, R. Chan, and M. H. Yap, "Facial Skin Classification using Convolutional Neural Networks", Proc. 14th Int. Conf. Image Anal. Recognit. (ICIAR 2017), LNCS, Vol. 10317, Springer, pp. 479-485, June, 2017. DOI: 10.1007/978-3-319-59876-5_53.
  16. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, December, 2016, pp. 2818-2826, DOI: 10.1109/CVPR.2016.308.
  17. B. Ahmad, M. Usama, C. Huang, K. Hwang, M. S. Hossain and G. Muhammad, "Discriminative Feature Learning for Skin Disease Classification Using Deep Convolutional Neural Network," IEEE Access, Vol. 8, pp. 39025-39033, IEEE, February, 2020. DOI: 10.1109/ACCESS.2020.2975198.
  18. C. Szegedy, S. Ioffe, V. Vanhoucke and A. A. Alemi, "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning", in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI 2017), pp. 4278-4284, AAAI Press, February, 2017.
  19. A. Pal, S. Ray, and U. Garain, "Skin Disease Identification from Dermoscopy Images using Deep Convolutional Neural Network," arXiv preprint arXiv:1807.09163, 2018.
  20. G. Huang, Z. Liu, L. V. D. Maaten, and K. Q. Weinberger, "Densely Connected Convolutional Networks," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, IEEE, pp. 2261-2269, July, 2017. DOI: 10.1109/CVPR.2017.243.
  21. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018, IEEE, pp. 4510-4520, June, 2018. DOI: 10.1109/CVPR.2018.00474.