Skin lesion segmentation using deep learning for images acquired from smartphones

被引:6
|
作者
De Angelo, Gabriel G. [1 ]
Pacheco, Andre G. C. [2 ]
Krohling, Renato A. [3 ]
机构
[1] Univ Fed Espirito Santo, Ave Fernando Ferrari,514, Vitoria, ES, Brazil
[2] Univ Fed Espirito Santo, Grad Program Comp Sci, Ave Fernando Ferrari,514, Vitoria, ES, Brazil
[3] Univ Fed Espirito Santo, UFES, Grad Program Comp Sci, Prod Engn Dept, Ave Fernando Ferrari,514, Vitoria, ES, Brazil
来源
2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2019年
关键词
Image Segmentation; Skin Cancer; Deep Learning; Fully Convolutional Network; BORDER DETECTION; DERMOSCOPY; EVOLUTIONARY;
D O I
10.1109/ijcnn.2019.8851803
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Skin lesion is an abnormal growth of skin cells that may become a skin cancer, which is a major health issue around the world and its incidence has been increasing throughout the years. Early detection is critical to increase the survival probability and, for economically emerging countries, it is a real problem, since there is a lack of specialists and medical tools. Smartphones are a low-cost device that may help to tackle this problem. However, acquiring a satisfactory amount of images taken using this tool is a hard task. In this sense, in partnership with a group of dermatologists, we developed an application to collect skin lesion images using smartphones' camera and create a new clinical dataset. In this work, we present a methodology using deep learning, color space combination and conditional random fields to segment the created dataset. We present a carefully investigation regarding the color spaces and the post-processing that allow us to raise some important remarks about the skin lesion ground truth images that strongly affect the final segmentation.
引用
收藏
页数:8
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