D-GET: Group-Enhanced Transformer for Diabetic Retinopathy Severity Classification in Fundus Fluorescein Angiography.
Early detection of Diabetic Retinopathy (DR) is vital for preserving vision and preventing deterioration of eyesight. Fundus Fluorescein Angiography (FFA), recognized as the gold standard for diagnosing DR, effectively reveals abnormalities in retinal vasculature. Given the labor-intensive and costl...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 16 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
3/6/2025
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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=183454771&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183454771 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 3/6/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 183454771 183454771 183454771 10.1007/s10916-025-02165-4 183454771 ppf: 1 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: D-GET: Group-Enhanced Transformer for Diabetic Retinopathy Severity Classification in Fundus Fluorescein Angiography. aug: au: Liu, Xina Xie, Jun Hou, Junjun Xu, Xinying Guo, Yan affil: https://ror.org/03kv08d37 College of Electrical and Power Engineering, Taiyuan University of Technology, 030002, Taiyuan, Shanxi, China sug: subj: Algorithms Utilization Deep Learning Diabetic Retinopathy Classification Diabetic Retinopathy Radiography Angiography Methods Fluorescent Dyes Severity of Illness Evaluation Diabetic Retinopathy Diagnosis Human Funding Source Early Diagnosis Retinal Diseases Prevention and Control Diagnostic Imaging Economics Convolutional Neural Networks Image Processing, Computer Assisted Machine Learning Factor Analysis Proxy Descriptive Statistics Data Analysis Software Data Curation Precision Sensitivity and Specificity Validity Radiographic Image Enhancement T-Tests Wilcoxon Signed Rank Test ab: Early detection of Diabetic Retinopathy (DR) is vital for preserving vision and preventing deterioration of eyesight. Fundus Fluorescein Angiography (FFA), recognized as the gold standard for diagnosing DR, effectively reveals abnormalities in retinal vasculature. Given the labor-intensive and costly nature of manual DR diagnosis, along with its low accuracy, developing a DR classification model based on FFA using deep learning techniques is crucial. Furthermore, DR classification faces challenges such as minimal lesion variance between different disease stages and significant size variations of lesions within the same stage, with small lesions often overlooked by existing models. We propose a deep learning model, D-GET, utilizing a Group-Enhanced Transformer for classifying DR lesion severity in FFA images. The D-GET model incorporates a Full-Scale Transformer Block, where the Group-Focal module captures feature information at multiple scales, from fine details to broader patterns, and adaptively integrates contextual information, enhancing the model's ability to detect small-scale lesions. The model also includes a Channel Adaptive Attention Module (CAAM) that synthesizes channel and spatial information to improve feature detection and localization. Experimental findings indicate that the D-GET method we developed surpasses existing methods on a custom dataset. The D-GET model, developed for DR classification using FFA images, significantly improves the detection of small-scale lesions. This advancement enhances the diagnosis and treatment of DR, establishing a solid foundation for its broader application across various domains of ophthalmic and general medical imaging. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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