MR Image Fusion-Based Parotid Gland Tumor Detection.
The differentiation of benign and malignant parotid gland tumors is of major significance as it directly affects the treatment process. In addition, it is also a vital task in terms of early and accurate diagnosis of parotid gland tumors and the determination of treatment planning accordingly. As in...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 3; pp. 1846 - 1860 |
|---|---|
| Autores principales: | , , , |
| Formato: | computer program diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2025
|
| 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=185280497&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185280497 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2025 vid: 38 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185280497 185280497 185280497 10.1007/s10278-024-01137-3 185280497 ppf: 1846 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: MR Image Fusion-Based Parotid Gland Tumor Detection. aug: au: Sunnetci, Kubilay Muhammed Kaba, Esat Celiker, Fatma Beyazal Alkan, Ahmet affil: https://ror.org/03h8sa373 Department of Electrical and Electronics Engineering, Osmaniye Korkut Ata University, 80000, Osmaniye, Turkey sug: subj: Parotid Neoplasms Diagnosis Magnetic Resonance Imaging Utilization Image Interpretation, Computer Assisted Human Graphical User Interface Sensitivity and Specificity Turkiye Deep Learning Funding Source ab: The differentiation of benign and malignant parotid gland tumors is of major significance as it directly affects the treatment process. In addition, it is also a vital task in terms of early and accurate diagnosis of parotid gland tumors and the determination of treatment planning accordingly. As in other diseases, the differentiation of tumor types involves several challenging, time-consuming, and laborious processes. In the study, Magnetic Resonance (MR) images of 114 patients with parotid gland tumors are used for training and testing purposes by Image Fusion (IF). After the Apparent Diffusion Coefficient (ADC), Contrast-enhanced T1-w (T1C-w), and T2-w sequences are cropped, IF (ADC, T1C-w), IF (ADC, T2-w), IF (T1C-w, T2-w), and IF (ADC, T1C-w, T2-w) datasets are obtained for different combinations of these sequences using a two-dimensional Discrete Wavelet Transform (DWT)-based fusion technique. For each of these four datasets, ResNet18, GoogLeNet, and DenseNet-201 architectures are trained separately, and thus, 12 models are obtained in the study. A Graphical User Interface (GUI) application that contains the most successful of these trained architectures for each data is also designed to support the users. The designed GUI application not only allows the fusing of different sequence images but also predicts whether the label of the fused image is benign or malignant. The results show that the DenseNet-201 models for IF (ADC, T1C-w), IF (ADC, T2-w), and IF (ADC, T1C-w, T2-w) are better than the others, with accuracies of 95.45%, 95.96%, and 92.93%, respectively. It is also noted in the study that the most successful model for IF (T1C-w, T2-w) is ResNet18, and its accuracy is equal to 94.95%. pubtype: Academic Journal doctype: computer program diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|