Classification of Diabetic Retinopathy Severity in Fundus Images Using the Vision Transformer and Residual Attention.
Comput Intell Neurosci
; 2023: 1305583, 2023.
Article
Dans Anglais
| MEDLINE | ID: covidwho-2194246
ABSTRACT
Diabetic retinopathy (DR) is a common retinal vascular disease, which can cause severe visual impairment. It is of great clinical significance to use fundus images for intelligent diagnosis of DR. In this paper, an intelligent DR classification model of fundus images is proposed. This method can detect all the five stages of DR, including of no DR, mild, moderate, severe, and proliferative. This model is composed of two key modules. FEB, feature extraction block, is mainly used for feature extraction of fundus images, and GPB, grading prediction block, is used to classify the five stages of DR. The transformer in the FEB has more fine-grained attention that can pay more attention to retinal hemorrhage and exudate areas. The residual attention in the GPB can effectively capture different spatial regions occupied by different classes of objects. Comprehensive experiments on DDR datasets well demonstrate the superiority of our method, and compared with the benchmark method, our method has achieved competitive performance.
Texte intégral:
Disponible
Collection:
Bases de données internationales
Base de données:
MEDLINE
Sujet Principal:
Diabète
/
Rétinopathie diabétique
Type d'étude:
Étude pronostique
Limites du sujet:
Humains
langue:
Anglais
Revue:
Comput Intell Neurosci
Thème du journal:
Informatique médicale
/
Neurologie
Année:
2023
Type de document:
Article
Pays d'affiliation:
2023
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