The Application of Deep Learning in the Risk Grading of Skin Tumors for Patients Using Clinical Images View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-07-13

AUTHORS

Xin-yu Zhao, Xian Wu, Fang-fang Li, Yi Li, Wei-hong Huang, Kai Huang, Xiao-yu He, Wei Fan, Zhe Wu, Ming-liang Chen, Jie Li, Zhong-ling Luo, Juan Su, Bin Xie, Shuang Zhao

ABSTRACT

According to diagnostic criteria, skin tumors can be divided into three categories: benign, low degree and high degree malignancy. For high degree malignant skin tumors, if not detected in time, they can do serious harm to patients’ health. However, in clinical practice, identifying malignant degree requires biopsy and pathological examination which is time costly. Furthermore, in many areas, due to the severe shortage of dermatologists, it’s inconvenient for patients to go to hospital for examination. Therefore, an easy to access screening method of malignant skin tumors is needed urgently. Firstly, we spend 5 years to build a dataset which includes 4,500 images of 10 kinds of skin tumors. All instances are verified pathologically thus trustworthy; Secondly, we label each instance to be either low-risk, high-risk or dangerous in which Junctional nevus, Intradermal nevus, Dermatofibroma, Lipoma and Seborrheic keratosis are low-risk, Basal cell carcinoma, Bowen’s disease and Actinic keratosis are high-risk, Squamous cell carcinoma and Malignant melanoma are dangerous; Thirdly, we apply the Xception architecture to build the risk degree classifier. The area under the curve (AUC) for three risk degrees reach 0.959, 0.919 and 0.947 respectively. To further evaluate the validity of the proposed risk degree classifier, we conduct a competition with 20 professional dermatologists. The results showed the proposed classifier outperforms dermatologists. Our system is helpful to patients in preliminary screening. It can identify the patients who are at risk and alert them to go to hospital for further examination. More... »

PAGES

283

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10916-019-1414-2

DOI

http://dx.doi.org/10.1007/s10916-019-1414-2

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PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/31300897


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