Prediction of Non-small Cell Lung Cancer Histology by a Deep Ensemble of Convolutional and Bidirectional Recurrent Neural Network.
Histology subtype prediction is a major task for grading non-small cell lung cancer (NSCLC) tumors. Invasive methods such as biopsy often lack in tumor sample, and as a result radiologists or oncologists find it difficult to detect proper histology of NSCLC tumors. The non-invasive methods such as m...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 4; pp. 895 - 903 |
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| Autores principales: | , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2020
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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=146122217&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146122217 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2020 vid: 33 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146122217 144134604 146122217 146122217 10.1007/s10278-020-00337-x 146122217 ppf: 895 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction of Non-small Cell Lung Cancer Histology by a Deep Ensemble of Convolutional and Bidirectional Recurrent Neural Network. aug: au: Moitra, Dipanjan Mandal, Rakesh Kumar affil: University of North Bengal, Siliguri, India sug: subj: Carcinoma, Non-Small-Cell Lung Pathology Neoplasm Grading Recurrent Neural Networks Human Diagnostic Imaging Biological Markers Deep Learning ROC Curve Carcinoma, Non-Small-Cell Lung Prognosis ab: Histology subtype prediction is a major task for grading non-small cell lung cancer (NSCLC) tumors. Invasive methods such as biopsy often lack in tumor sample, and as a result radiologists or oncologists find it difficult to detect proper histology of NSCLC tumors. The non-invasive methods such as machine learning may play a useful role to predict NSCLC histology by using medical image biomarkers. Few attempts have so far been made to predict NSCLC histology by considering all the major subtypes. The present study aimed to develop a more accurate deep learning model by clubbing convolutional and bidirectional recurrent neural networks. The NSCLC Radiogenomics dataset having 211 subjects was used in the study. Ten best models found during experimentation were averaged to form an ensemble. The model ensemble was executed with 10-fold repeated stratified cross-validation, and the results got were tested with metrics like accuracy, recall, precision, F1-score, Cohen's kappa, and ROC-AUC score. The accuracy of the ensemble model showed considerable improvement over the best model found with the single model. The proposed model may help significantly in the automated prognosis of NSCLC and other types of cancers. 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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