Multidimensional CNN-Based Deep Segmentation Method for Tumor Identification.
Weighted MR images of 421 patients with nasopharyngeal cancer were obtained at the head and neck level, and the tumors in the images were assessed by two expert doctors. 346 patients' multimodal pictures and labels served as training sets, whereas the remaining 75 patients' multimodal images and lab...
| Publicado en: | BioMed Research International pp. 1 - 12 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research Journal Article |
| Publicado: |
Wiley-Blackwell
8/21/2022
|
| 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=158630388&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158630388 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/21/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158630388 158630388 158630388 10.1155/2022/5061112 158630388 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multidimensional CNN-Based Deep Segmentation Method for Tumor Identification. aug: au: Martin, R. John Sharma, Uttam Kaur, Kiranjeet Kadhim, Noor Mohammed Lamin, Madonna Ayipeh, Collins Sam affil: Faculty of Computer Science and Information Technology, Jazan University, Saudi Arabia sug: subj: Nasopharyngeal Neoplasms Diagnosis Neural Networks (Computer) Head Radiography Neck Radiography Magnetic Resonance Imaging Methods Imaging, Three-Dimensional Methods Human Nasopharyngeal Neoplasms Radiography Cancer Patients Models, Structural Comparative Studies Diagnostic Reasoning Clinical Reasoning Physicians Expert Clinicians ab: Weighted MR images of 421 patients with nasopharyngeal cancer were obtained at the head and neck level, and the tumors in the images were assessed by two expert doctors. 346 patients' multimodal pictures and labels served as training sets, whereas the remaining 75 patients' multimodal images and labels served as independent test sets. Convolutional neural network (CNN) for modal multidimensional information fusion and multimodal multidimensional information fusion (MMMDF) was used. The three models' performance is compared, and the findings reveal that the multimodal multidimensional fusion model performs best, while the two-modal multidimensional information fusion model performs second. The single-modal multidimensional information fusion model has the poorest performance. In MR images of nasopharyngeal cancer, a convolutional network can precisely and efficiently segment tumors. pubtype: Academic Journal doctype: diagnostic images equations & formulas research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|