The Application of Deep Learning in the Risk Grading of Skin Tumors for Patients Using Clinical Images
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.
KeywordsConvolutional neural network Skin tumor Risk grade Preliminary screening
First, second and third authors contributed equally to this paper. Authors are grateful to all the doctors and nurses in Department of Dermatology, Xiangya Hospital Central South University.
This research was funded by National Key R&D Program of China (2018YFC0117000), Specialized Basic Work of Science and Technology (2015FY111100) and Hunan Provincial Science and Technology Department (2018SK2092).
Compliance with Ethical Standards
All the authors of this article are aware of the content.
Conflict of Interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants performed by any of the authors.
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