A Comparison of the Efficiency of Using a Deep CNN Approach with Other Common Regression Methods for the Prediction of EGFR Expression in Glioblastoma Patients.
To estimate epithermal growth factor receptor (EGFR) expression level in glioblastoma (GBM) patients using radiogenomic analysis of magnetic resonance images (MRI). A comparative study using a deep convolutional neural network (CNN)–based regression, deep neural network, least absolute shrinkage and...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 2; pp. 391 - 399 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2020
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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=142764025&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142764025 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2020 vid: 33 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142764025 142764025 142764025 10.1007/s10278-019-00290-4 142764025 ppf: 391 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Comparison of the Efficiency of Using a Deep CNN Approach with Other Common Regression Methods for the Prediction of EGFR Expression in Glioblastoma Patients. aug: au: Hedyehzadeh, Mohammadreza Maghooli, Keivan MomenGharibvand, Mohammad Pistorius, Stephen affil: Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran sug: subj: Deep Learning Neural Networks (Computer) Epidermal Growth Factors Blood Receptors, Cell Surface Blood Gene Expression Evaluation Glioma Diagnosis Magnetic Resonance Imaging Methods Human Comparative Studies Linear Regression ab: To estimate epithermal growth factor receptor (EGFR) expression level in glioblastoma (GBM) patients using radiogenomic analysis of magnetic resonance images (MRI). A comparative study using a deep convolutional neural network (CNN)–based regression, deep neural network, least absolute shrinkage and selection operator (LASSO) regression, elastic net regression, and linear regression with no regularization was carried out to estimate EGFR expression of 166 GBM patients. Except for the deep CNN case, overfitting was prevented by using feature selection, and loss values for each method were compared. The loss values in the training phase for deep CNN, deep neural network, Elastic net, LASSO, and the linear regression with no regularization were 2.90, 8.69, 7.13, 14.63, and 21.76, respectively, while in the test phase, the loss values were 5.94, 10.28, 13.61, 17.32, and 24.19 respectively. These results illustrate that the efficiency of the deep CNN approach is better than that of the other methods, including Lasso regression, which is a regression method known for its advantage in high-dimension cases. A comparison between deep CNN, deep neural network, and three other common regression methods was carried out, and the efficiency of the CNN deep learning approach, in comparison with other regression models, was demonstrated. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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