Automatic Glaucoma Diagnosis Based on Photo Segmentation with Fundus Images

Raja, P. M. Siva; Sumithra, R.; G, Thanusha

doi:10.1109/iccike51210.2021.9410772

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Siva Raja et al, applied computer vision techniques for the detection of glaucoma. The technique combined the set of image noise-reducing filters and the ensemble of clustering algorithms [ 9 ]. Dharmawan et al, developed a computer-aided system that was used to investigate micro-vessels, vessels with a central reflex and vessels in the presence of lesions.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Microaneurysms in Fundus Images Using an Ensemble-Based Segmentation Method

Raudonis

Kairys

Verkauskienė

et al. 2023

Sensors

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In this study, a novel method for automatic microaneurysm detection in color fundus images is presented. The proposed method is based on three main steps: (1) image breakdown to smaller image patches, (2) inference to segmentation models, and (3) reconstruction of the predicted segmentation map from output patches. The proposed segmentation method is based on an ensemble of three individual deep networks, such as U-Net, ResNet34-UNet and UNet++. The performance evaluation is based on the calculation of the Dice score and IoU values. The ensemble-based model achieved higher Dice score (0.95) and IoU (0.91) values compared to other network architectures. The proposed ensemble-based model demonstrates the high practical application potential for detection of early-stage diabetic retinopathy in color fundus images.

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Section: Introductionmentioning

confidence: 99%