[논문]An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network

Shen, Xiaolei; Zhang, Jiachi; Yan, Chenjun; Zhou, Hong

doi:10.1038/s41598-018-24204-6

An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network 원문보기

Scientific reports, v.8, 2018년, pp.5839 - 5839

Shen, Xiaolei (Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, HangZhou, 310027, China) , Zhang, Jiachi (Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, HangZhou, 310027, China) , Yan, Chenjun (Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, HangZhou, 310027, China) , Zhou, Hong (Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, HangZhou, 310027, China. zhouh@mail.bme.zju.edu.cn)

Abstract ▼ AI-Helper

In this paper, we present a new automatic diagnosis method for facial acne vulgaris which is based on convolutional neural networks (CNNs). To overcome the shortcomings of previous methods which were the inability to classify enough types of acne vulgaris. The core of our method is to extract features of images based on CNNs and achieve classification by classifier. A binary-classifier of skin-and-non-skin is used to detect skin area and a seven-classifier is used to achieve the classification task of facial acne vulgaris and healthy skin. In the experiments, we compare the effectiveness of our CNN and the VGG16 neural network which is pre-trained on the ImageNet data set. We use a ROC curve to evaluate the performance of binary-classifier and use a normalized confusion matrix to evaluate the performance of seven-classifier. The results of our experiments show that the pre-trained VGG16 neural network is effective in extracting features from facial acne vulgaris images. And the features are very useful for the follow-up classifiers. Finally, we try applying the classifiers both based on the pre-trained VGG16 neural network to assist doctors in facial acne vulgaris diagnosis.

참고문헌 (31)

1. Williams HC Dellavalle RP Garner S Acne vulgaris The Lancet 2012 379 361 372 10.1016/S0140-6736(11)60321-8

상세보기
2. Bhate K Williams H Epidemiology of acne vulgaris British Journal of Dermatology 2013 168 474 485 10.1111/bjd.12149 23210645

상세보기
3. Hsu R-L Abdel-Mottaleb M Jain AK Face detection in color images IEEE transactions on pattern analysis and machine intelligence 2002 24 696 706 10.1109/34.1000242

상세보기
4. Phung, S. L., Bouzerdoum, A. & Chai, D. A novel skin color model in ycbcr color space and its application to human face detection. In Image Processing . 2002 . Proceedings . 2002 International Conference on , vol. 1, I–I (IEEE, 2002).
5. Vezhnevets, V., Sazonov, V. & Andreeva, A. A survey on pixel-based skin color detection techniques. In Proc . Graphicon , vol. 3, 85–92 (Moscow, Russia, 2003).
6. Phung SL Bouzerdoum A Chai D Skin segmentation using color pixel classification: analysis and comparison IEEE transactions on pattern analysis and machine intelligence 2005 27 148 154 10.1109/TPAMI.2005.17 15628277

상세보기
7. Jones MJ Rehg JM Statistical color models with application to skin detection International Journal of Computer Vision 2002 46 81 96 10.1023/A:1013200319198
8. Kim, I., Shim, J. H. & Yang, J. Face detection. Face Detection Project, EE368, Stanford University 28 , 538 (2003).
9. Chantharaphaichi, T., Uyyanonvara, B., Sinthanayothin, C. & Nishihara, A. Automatic acne detection for medical treatment. In Information and Communication Technology for Embedded Systems (IC-ICTES ) , 2015 6th International Conference of , 1–6 (IEEE, 2015).
10. Kittigul, N. & Uyyanonvara, B. Automatic acne detection system for medical treatment progress report. In Information and Communication Technology for Embedded Systems (IC-ICTES ) , 2016 7th International Conference of , 41–44 (IEEE, 2016).
11. Chang, C.-Y. & Liao, H.-Y. Automatic facial skin defects detection and recognition system. In Genetic and Evolutionary Computing (ICGEC ) , 2011 Fifth International Conference on , 260–263 (IEEE, 2011).
12. Malik, A. S. et al . Digital assessment of facial acne vulgaris. In Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2014 IEEE International, 546–550 (IEEE, 2014).
13. Ciregan, D., Meier, U. & Schmidhuber, J. Multi-column deep neural networks for image classification. In Computer vision and pattern recognition (CVPR ) , 2012 IEEE conference on , 3642–3649 (IEEE, 2012).
14. LeCun Y Backpropagation applied to handwritten zip code recognition Neural computation 1989 1 541 551 10.1162/neco.1989.1.4.541

상세보기
15. Sermanet, P., Chintala, S. & LeCun, Y. Convolutional neural networks applied to house numbers digit classification. In Pattern Recognition (ICPR), 2012 21st International Conference on , 3288–3291 (IEEE, 2012).
16. Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207 . 0580 (2012).
17. Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems , 1097–1105 (2012).
18. He, K., Zhang, X., Ren, S. & Sun, J. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision , 1026–1034 (2015).
19. LeCun, Y., Kavukcuoglu, K. & Farabet, C. Convolutional networks and applications in vision. In Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on , 253–256 (IEEE, 2010).
20. Cireşan, D. C., Meier, U., Masci, J., Gambardella, L. M. & Schmidhuber, J. High-performance neural networks for visual object classification. arXiv preprint arXiv : 1102 . 0183 (2011).
21. Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv : 1409 . 1556 (2014).
22. Fawcett T An introduction to roc analysis Pattern recognition letters 2006 27 861 874 10.1016/j.patrec.2005.10.010

상세보기
23. Hajian-Tilaki K Receiver operating characteristic (roc) curve analysis for medical diagnostic test evaluation Caspian journal of internal medicine 2013 4 627 24009950

상세보기
24. Fluss R Faraggi D Reiser B Estimation of the youden index and its associated cutoff point Biometrical journal 2005 47 458 472 10.1002/bimj.200410135 16161804

상세보기
25. Pedregosa F Scikit-learn: Machine learning in python Journal of machine learning research 2011 12 2825 2830
26. Chen X Yan CC Zhang X You Z-H Long non-coding rnas and complex diseases: from experimental results to computational models Briefings in bioinformatics 2016 18 558 576
27. Chen X Yan G-Y Novel human lncrna–disease association inference based on lncrna expression profiles Bioinformatics 2013 29 2617 2624 10.1093/bioinformatics/btt426 24002109

상세보기
28. Chen, X., Xie, D., Zhao, Q. & You, Z.-H. Micrornas and complex diseases: from experimental results to computational models. Briefings in bioinformatics (2017).
29. Chen X Niu Y-W Wang G-H Yan G-Y Hamda: Hybrid approach for mirna-disease association prediction Journal of biomedical informatics 2017 76 50 58 10.1016/j.jbi.2017.10.014 29097278

상세보기
30. Chen X Huang L Lrsslmda: Laplacian regularized sparse subspace learning for mirna-disease association prediction PLoS computational biology 2017 13 e1005912 10.1371/journal.pcbi.1005912 29253885

상세보기
31. You Z-H Pbmda: A novel and effective path-based computational model for mirna-disease association prediction PLoS computational biology 2017 13 e1005455 10.1371/journal.pcbi.1005455 28339468

상세보기

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An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network 원문보기

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