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

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

Publication date: Jul 13, 2019

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.

Zhao, X.Y., Wu, X., Li, F.F., Li, Y., Huang, W.H., Huang, K., He, X.Y., Fan, W., Wu, Z., Chen, M.L., Li, J., Luo, Z.L., Su, J., Xie, B., and Zhao, S. The Application of Deep Learning in the Risk Grading of Skin Tumors for Patients Using Clinical Images. 23364. 2019 J Med Syst (43):8.

Concepts Keywords
Actinic Keratosis Neural network
AUC Carcinoma Malignant melanoma
Benign Cutaneous conditions
Biopsy Organ systems
Carcinoma RTT
Classifier Medicine
Deep Learning Seborrheic keratosis
Hospital Melanoma
Lipoma Gynaecological cancer
Malignancy Actinic keratosis
Malignant Dermatofibroma
Malignant Melanoma Dysplastic nevus
Nevus Neural network
Pathological Examination
Squamous Carcinoma

Semantics

Type Source Name
disease DOID Malignant melanoma
disease MESH Malignant melanoma
disease DOID Squamous cell carcinoma
disease MESH Squamous cell carcinoma
disease DOID Actinic keratosis
disease MESH Actinic keratosis
pathway BSID Basal cell carcinoma
disease DOID Basal cell carcinoma
disease MESH Basal cell carcinoma
disease DOID Seborrheic keratosis
disease MESH Seborrheic keratosis
disease DOID Lipoma
disease MESH Lipoma
disease DOID Dermatofibroma
disease MESH Dermatofibroma
disease MESH nevus
disease MESH nevus Intradermal
disease MESH Tumors

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