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Radiomic-Based Features for Diabetic Retinopathy Early Detection Using Deep Neural Networks
7/7 checkable references clean · checked 2026-09-09
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
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The 7 checked references that resolve
resolves10.3390/jpm12020317
Automatic Microaneurysms Detection for Early Diagnosis of Diabetic Retinopathy Using Improved Discrete Particle Swarm Optimization
resolves10.1109/ACCESS.2019.2947484
A Deep Learning Ensemble Approach for Diabetic Retinopathy Detection
resolves10.1016/j.ophtha.2021.04.027
Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045
resolves10.1158/0008-5472.CAN-17-0339
Computational Radiomics System to Decode the Radiographic Phenotype
The 9 references without a DOI — listed, not checked
no DOI — not checkedref1
no DOI — not checkedref3
no DOI — not checkedref4
no DOI — not checkedref5
no DOI — not checkedDiabetic retinopathy classification using a modified xception architecture
no DOI — not checkedDifferent fundus imaging modalities and technical factors in ai screening for diabetic retinopathy: a review
no DOI — not checkedDetection of diabetic retinopathy using deep learning methodology
no DOI — not checkedref10
no DOI — not checkedEarly detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images
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