ArticleScientific reports2025
Diabetic retinopathy detection based on mobile maxout network and weber local descriptor feature selection using retinal fundus image.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A systematic review on deep learning techniques for diabetic retinopathy classification in retinal fundus images.iScience · 2026Article
- Diabetic Retinopathy Detection: AI Models and Approaches.Journal of ophthalmology · 2026Review
- Learning feature dependencies for precise tumor region detection and segmentation in optical coherence tomography images.International ophthalmology · 2025Article
- Automated Diagnosis of Hypertensive Retinopathy Using Res-UNet and Graph Convolutional Networks.Healthcare technology lettersArticle
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Authors and funding
8 authors.
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Abstract
Retinal screening provides for earlier detection of diabetic retinopathy (DR) as well as prompt diagnosis. Recognizing DR utilizing color fundus imaging needs qualified specialists to know about the presence and significance of a few insignificant features that when it linked with complicated categorization structure create this as an engaging and difficult task. The automatic progression of DR detection consumes more time and cost. To conquer these gaps, a hybrid network structure for DR detection utilizing retinal fundus image named Mobile Maxout network (MM-Net). Here, MM-Net is merged with the merging of MobileNet and Deep Maxout Network (DMN). At first, the input retinal image is pre-processed by utilizing a median filter. Then, optic disk (OD) segmentation progress is done by utilizing the active contour model as well as the filtered image is also passed through blood vessel segmentation that is progressed by O-SegNet. Afterwards, the segmented and input images are allowed into the feature extraction phase. Finally, DR detection is achieved by the proposed MM-Net. The analytic metrics deployed for MM-Net, such as accuracy, sensitivity and specificity achieved 89.2%, 90.5%, and 92.0%.
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