Gawas, P.Sowmya Kamath, S.2026-02-042023International Journal of Biomedical Engineering and Technology, 2023, 43, 1, pp. 60-7517526418https://doi.org/10.1504/IJBET.2023.133723https://idr.nitk.ac.in/handle/123456789/22102Diabetic retinopathy (DR) is a complication caused by increased blood glucose levels, which causes retinal damage in diabetic patients’ eyes. If not discovered and treated early, it can lead to vision loss. Hard exudates (HE) are one of its characteristic signs. Identification of HE is a paramount step in early diagnosis of DR. In this work, the suitability of U-Net-based deep CNN with different encoder configurations and attention gates (AG) is experimented, for HE segmentation. The proposed models were benchmarked on the standard IDRiD dataset. To overcome the challenges related to the limited dataset, data augmentation techniques were also applied to generate image patches and used for model training. Extensive experiments on the dataset revealed that U-Net with AG achieved an accuracy of 98.8%. The U-Net with ResNet50 as the encoder backbone achieved an accuracy of 98.64%. The findings show that the presented models are effective and suitable for early-stage clinical diagnosis. © © 2023 Inderscience Enterprises Ltd.Deep learningEye protectionMedical informaticsSignal encodingAttention mechanismsDiabetic retinopathyDiabetic retinopathy predictionHard exudate segmentationHard exudatesNeural encoderDiagnosisArticleartificial neural networkbenchmarkingconvolutional neural networkdata processingdeep neural networkdiabetic retinopathydiagnostic accuracyearly diagnosiseye fundusfeature extractionfuzzy c means clusteringhard exudatehumanimage analysisimage segmentationmachine learningmethodologyophthalmologistprognosisresidual neural networkretina exudateretina imagesupport vector machineworkflow