Bibliographic Details
| Title: |
Multidimensional CNN-Based Deep Segmentation Method for Tumor Identification. |
| Authors: |
Martin, R. John1 (AUTHOR), Sharma, Uttam2 (AUTHOR), Kaur, Kiranjeet3 (AUTHOR), Kadhim, Noor Mohammed4 (AUTHOR), Lamin, Madonna5 (AUTHOR), Ayipeh, Collins Sam6 (AUTHOR) |
| Source: |
BioMed Research International. 8/21/2022, p1-11. 11p. |
| Subject Terms: |
*CRANIAL radiography, *THREE-dimensional imaging, *STRUCTURAL models, *MAGNETIC resonance imaging, *MEDICAL personnel, *CANCER patients, *COMPARATIVE studies, *DECISION making, *DIAGNOSIS, *EXPERTISE, *ARTIFICIAL neural networks, *PHYSICIANS, *MEDICAL logic, NECK radiography, NASOPHARYNX tumors |
| Abstract: |
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. [ABSTRACT FROM AUTHOR] |
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| Database: |
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