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Skin lesion segmentation method for dermoscopic images with convolutional neural networks and semantic segmentation
Dang N.H. Thanh 1, Nguyen Hoang Hai 2, Le Minh Hieu 3, Prayag Tiwari 4, V.B. Surya Prasath 5,6,7,8

Department of Information Technology, School of Business Information Technology, University of Economics Ho Chi Minh City, Vietnam,

Faculty of Computer Science, Vietnam-Korea University of Information and Communication Technology – The Uni-versity of Danang, Vietnam,

Department of Economics, University of Economics, University of Danang, Vietnam,

Department of Information Engineering, University of Padua, Italy,

Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA,

Department of Pediatrics, University of Cincinnati, OH USA,

Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH USA,

Department of Electrical Engineering and Computer Science, University of Cincinnati, OH USA

 PDF, 1051 kB

DOI: 10.18287/2412-6179-CO-748

Страницы: 122-129.

Язык статьи: English

Melanoma skin cancer is one of the most dangerous forms of skin cancer because it grows fast and causes most of the skin cancer deaths. Hence, early detection is a very important task to treat melanoma. In this article, we propose a skin lesion segmentation method for dermoscopic images based on the U-Net architecture with VGG-16 encoder and the semantic segmentation. Base on the segmented skin lesion, diagnostic imaging systems can evaluate skin lesion features to classify them. The proposed method requires fewer resources for training, and it is suitable for computing systems without powerful GPUs, but the training accuracy is still high enough (above 95 %). In the experiments, we train the model on the ISIC dataset – a common dermoscopic image dataset. To assess the performance of the proposed skin lesion segmentation method, we evaluate the Sorensen-Dice and the Jaccard scores and compare to other deep learning-based skin lesion segmentation methods. Experimental results showed that skin lesion segmentation quality of the proposed method are better than ones of the compared methods.

Ключевые слова:
image segmentation, medical image segmentation, semantic segmentation, melanoma, skin cancer, skin lesion, deep learning, cancer.

This research was funded by University of Economics Ho Chi Minh City, Vietnam.

Thanh DNH, Hai NH, Hieu LM, Tiwari P, Prasath VBS. Skin lesion segmentation method for dermoscopic images with convolutional neural networks and semantic segmentation. Computer Optics 2021; 45(1): 122-129. DOI: 10.18287/2412-6179-CO-748.


  1. Kunz M, Stolz W. ABCD rule. Source: <https://dermoscopedia.org/ABCD_rule>.
  2. Thanh DNH, Erkan U, Prasath VBS, Kumar V, Hien NN. A skin lesion segmentation method for dermoscopic images based on adaptive thresholding with normalization of color models. IEEE 2019 6th International Conference on Electrical and Electronics Engineering 2019: 116-120.
  3. Yuan Y, Lo YC. Improving dermoscopic image segmentation with enhanced convolutional-deconvolutional networks. IEEE J Biomed Health Inform 2017; 23(2): 519-526.
  4. Yuan Y, Chao M, Lo Y-C. Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance. IEEE Trans Med Imaging 2017; 36(9): 1876-1886.
  5. Thanh DNH, Hien NN, Prasath VBS, Thanh LT, Hai NH. Automatic initial boundary generation methods based on edge detectors for the level set function of the chan-vese segmentation model and applications in biomedical image processing. In Book: Satapathy SC, Bhateja V, Nguyen BL, Nguyen NG, Le D-N, eds. Frontiers in intelligent computing: theory and applications. Singapore: Springer: 2020: 171-181.
  6. Bi L, Kim J, Ahn E, Feng D. Automatic skin lesion analysis using large-scale dermoscopy images and deep residual networks. arXiv preprint 2017. Source: <https://arxiv.org/abs/1703.04197>.
  7. Bi L, Kim J, Ahn E, Kumar A, Fulham M, Feng D. Dermoscopic image segmentation via multistage fully convolutional networks. IEEE Trans Biomed Eng 2017; 64(9): 2065-2074.
  8. Chen Y, Cao Z, Cao C, Yang J, Zhang J. A modified U-Net for Brain MR image segmentation. In Book: Sun X, Pan Z, Bertino E, eds. Cloud computing and security. Cham: Springer; 2018: 233-242.
  9. Xie F, Yang J, Liu J, Jiang Z, Zheng Y, Wang Y. Skin lesion segmentation using high-resolution convolutional neural network. Comput Methods Programs Biomed 2020;  186: 105241.
  10. Bozorgtabar B, Sedai S, Roy PK, Garnavi R. Skin lesion segmentation using deep convolution networks guided by local unsupervised learning. IBM J Res Dev 2017; 61(4-5): 6.
  11. Li H, He X, Zhou F, Yu Z, Ni D, Chen S, Wang T, Lei B. Dense deconvolutional network for skin lesion segmentation. IEEE J Biomed Health Inform 2019; 23(2): 527-537.
  12. He X, Yu Z, Wang T, Lei B, Shi Y. Dense deconvolution net: Multi path fusion and dense deconvolution for high resolution skin lesion segmentation. Technol Health Care 2018; 26: 307-316.
  13. Goyal M, Oakley A, Bansal P, Dancey D, Yap MH. Skin lesion segmentation in dermoscopic images with ensemble deep learning methods. IEEE Access 2020; 8: 4171-4181.
  14. Zafar K, Gilani SO, Waris A, Ahmed A, Jamil M, Khan MN, Kashif AS. Skin lesion segmentation from dermoscopic images using convolutional neural network. Sensors 2020; 20(6): 1601.
  15. Li Y, Shen L. Skin lesion analysis towards melanoma detection using deep learning network. Sensors 2018; 18(2): 556.
  16. Mishra R, Daescu O. Deep learning for skin lesion segmentation. IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2017: 1189-1194.
  17. Yao Y, Luo Z, Li S, Fang T, Quan L. MVSNet: Depth inference for unstructured multi-view stereo. In Book: Ferrari V, Hebert M, Sminchisescu C, Weiss Y, eds.Computer Vision – ECCV 2018. Cham: Springer; 2018: 785-801.
  18. Karen S, Zisserman A. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations (ICLR-2015) 2015.
  19. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM 2017; 60(6): 84-90.
  20. Brostow GJ, Julien F, Roberto C. Semantic object classes in video: a high-definition ground truth database. Patt Recogn Lett 2008; 30(2): 88-97.
  21. Le Cun Y, Boser B, Denker J, Henderson D, Howard RE, Hubbard W, Jackel L. Handwritten digit recognition with a back-propagation network. In Book: Touretzky DS, ed. Advances in neural information processing systems 2. San Francisco: Morgan Kaufmann; 1990: 396-404.
  22. Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on International Conference on Machine Learning (ICML'15) 2015: 448-456.
  23. Vinod N, Hinton GE. Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th international conference on machine learning (ICML-10) 2010: 807-814.
  24. Nagi J, Ducatelle F, Di-Caro GA, Ciresan D, Meier U, Giusti A, Nagi F, Schmidhuber J, Gambardella LM. Max-pooling convolutional neural networks for vision-based hand gesture recognition. IEEE International Conference on Signal and Image Processing Applications (ICSIPA2011) 2011: 342-347.
  25. Bishop CM. Pattern recognition and machine learning, New York: Springer, 2006.
  26. Csurka G, Larlus D, Perronnin F. What is a good evaluation measure for semantic segmentation. The British Machine Vision Conference 2013.
  27. Thanh DNH, Prasath VBS, Hieu LM, Hien NN. Melanoma skin cancer detection method based on adaptive principal curvature, colour normalisation and feature extraction with the ABCD rule. J Digit Imaging 2020; 33: 574-585.
  28. Abdel AT, Allan H. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Medical Imaging 2015; 15: 1-29.
  29. Qian N. On the momentum term in gradient descent learning algorithms. Neural Netw 1999; 12(1): 145-151.
  30. Berseth M. ISIC 2017 – Skin lesion analysis towards melanoma detection. arXiv preprint 2017. Source: <https://arxiv.org/abs/1703.00523>.
  31. Thanh DNH, Prasath VBS, Son NV, Hieu LM. An adaptive image inpainting method based on the modified mumford-shah model and multiscale parameter estimation. Computer Optics 2019; 43(2): 251-257. DOI: 10.18287/2412-6179-2019-43-2-251-257.
  32. Thanh DNH, Prasath VBS, Dvoenko S, Hieu LM. An adaptive image inpainting method based on euler's elastica with adaptive parameters estimation and the discrete gradient method. Signal Process 2021; 178: 107797.

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