Interpretable deep automation system for graded diagnosis of diabetic retinopathy
Authors:
- Pattabiraman VenkatasubbuPVPattabiraman Venkatasubbu
- A.V Shreyas MadhavAMA.V Shreyas Madhav
- K PrakashKPK Prakash
- Raghav RajaramanRRRaghav Rajaraman
- Lokavarapu Bhaskara RaoLRLokavarapu Bhaskara Rao
- 12 August 2025
- journal article
- Published by Elsevier in Biomedical Signal Processing and Control
- Vol. 112
Abstract
No abstract availableThis publication has 22 references indexed in Scilit:
- Sparse spectrum based swarm decomposition for robust nonstationary signal analysis with application to sleep apnea detection from EEGBiomedical Signal Processing and Control, 2022
- Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classificationJournal of Ambient Intelligence and Humanized Computing, 2021
- Screening for diabetic retinopathy: new perspectives and challengesThe Lancet Diabetes & Endocrinology, 2020
- Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in IndiaJAMA Ophthalmology, 2019
- Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging ApplicationCurrent Diabetes Reports, 2019
- Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic RetinopathyOphthalmology, 2019
- Diabetic Retinopathy: Present and PastProcedia Computer Science, 2018
- Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual featuresMedical & Biological Engineering & Computing, 2017
- Practical Manual of Diabetic Retinopathy Management2017
- Diabetic Retinopathy Detection Based on Eigenvalues of the Hessian MatrixProcedia Computer Science, 2015