Attention Layer-Based Multidimensional Feature Extraction for Diagnosis of Lung Cancer.
At present, early lung cancer screening is mainly based on radiologists' experience in diagnosing benign and malignant pulmonary nodules by lung CT images. On the other hand, intraoperative rapid freezing pathology needs to analyse the invasive adenocarcinoma nodules with the worst recovery in adeno...
| Publicado en: | BioMed Research International pp. 1 - 12 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
7/4/2022
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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=157800562&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157800562 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 7/4/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 157800562 157800562 157800562 10.1155/2022/3947434 157800562 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Attention Layer-Based Multidimensional Feature Extraction for Diagnosis of Lung Cancer. aug: au: Bhende, Manisha Thakare, Anuradha Saravanan, V. Anbazhagan, K. Patel, Hemant N. Kumar, Ashok affil: Marathwada Mitra Mandal's Institute of Technology, Pune, India sug: subj: Lung Neoplasms Radiography Adenocarcinoma of Lung Radiography Tomography, X-Ray Computed Image Processing, Computer Assisted Predictive Value of Tests Human Algorithms Neural Networks (Computer) Software Design Machine Learning Imaging, Three-Dimensional Validity Sensitivity and Specificity ab: At present, early lung cancer screening is mainly based on radiologists' experience in diagnosing benign and malignant pulmonary nodules by lung CT images. On the other hand, intraoperative rapid freezing pathology needs to analyse the invasive adenocarcinoma nodules with the worst recovery in adenocarcinoma. Moreover, rapid freezing pathology has a low diagnostic accuracy for small-diameter nodules. Because of the above problems, an algorithm for diagnosing invasive adenocarcinoma nodules in ground-glass pulmonary nodules is based on CT images. According to the nodule space information and plane features, sample data of different dimensions are designed, namely, 3D space and 2D plane feature samples. The network structure is designed based on the attention mechanism and residual learning unit; 2D and 3D neural networks are along built. By fusing the feature vectors extracted from networks of different dimensions, the diagnosis results of invasive adenocarcinoma nodules are finally obtained. The algorithm was studied on 1760 ground-glass nodules with 5-20 mm diameter collected from a city chest hospital with surgical and pathological results. There were 340 nodules with invasive adenocarcinoma and 340 with noninvasive adenocarcinoma. A total of 1420 invasive nodule samples were cross-validated on this example dataset. The classification accuracy of the algorithm was 82.7%, the sensitivity was 82.9%, and the specificity was 82.6%. 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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