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...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 8 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137490069&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137490069 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Aug2019 vid: 43 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137490069 137490069 137490069 10.1007/s10916-019-1414-2 137490069 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: The Application of Deep Learning in the Risk Grading of Skin Tumors for Patients Using Clinical Images. aug: au: Zhao, Xin-yu Wu, Xian Li, Fang-fang Li, Yi Huang, Wei-hong Huang, Kai He, Xiao-yu Fan, Wei Wu, Zhe Chen, Ming-liang Li, Jie Luo, Zhong-ling Su, Juan Xie, Bin Zhao, Shuang affil: School of Automation, Central South University, Changsha, China sug: subj: Skin Neoplasms Prevention and Control Skin Neoplasms Classification Risk Assessment Methods Cancer Screening Methods Deep Learning Digital Imaging Utilization Human Early Detection of Cancer Neural Networks (Computer) ROC Curve Algorithms Funding Source ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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