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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

1 Department of Information Technology, School of Business Information Technology,
University of Economics Ho Chi Minh City, Vietnam,
2 Faculty of Computer Science, Vietnam-Korea University of Information and Communication Technology –
The University of Danang, Vietnam,
3 Department of Economics, University of Economics, University of Danang, Vietnam,
4 Department of Information Engineering, University of Padua, Italy,
5 Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA,
6 Department of Pediatrics, University of Cincinnati, OH USA,
7 Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH USA,
8 Department of Electrical Engineering and Computer Science, University of Cincinnati, OH USA

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DOI: 10.18287/2412-6179-CO-748

Pages: 122-129.

Full text of article: English language.

Abstract:
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.

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

Citation:
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.

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

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