Exudate identification in retinal fundus images using precise textural verifications

Monemian, Maryam; Rabbani, Hossein

doi:10.1038/s41598-023-29916-y

Cited by 7 publications

(1 citation statement)

References 41 publications

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“…Many researchers [6]- [8] have misclassified lesion detection, and it is difficult to establish appropriate classes for lesion detection from retinal fundus images. Deep learning-based methods especially fully convolutional neural networks have achieved great success in medical image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Dual-Branch U-Net Architecture for Retinal Lesions Segmentation on Fundus Image

Yin,

Soomro,

Jandan

et al. 2023

IEEE Access

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Deep learning has found widespread application in diabetic retinopathy (DR) screening, primarily for lesion detection. However, this approach encounters challenges such as information loss due to convolutional operations, shape uncertainty, and the high similarity between different lesion types. These factors collectively hinder the accurate segmentation of lesions. In this research paper, we introduce a novel dual-branch U-Net architecture, referred to as Dual-Branch (DB)-U-Net, tailored to address the intricacies of small-scale lesion segmentation. Our approach involves two branches: one employs a U-Net to capture the shared characteristics of lesions, while the other utilizes a modified U-Net, known as U2Net, equipped with two decoders that share a common encoder. U2Net is responsible for generating probability maps for lesion segmentation as well as corresponding boundary segmentation. DB U-Net combines the outputs of U2Net and U-Net as a dual branch, concatenating their segmentation maps to produce the final result. To mitigate the challenge of imbalanced data, we employ the Dice loss as a loss function. We evaluate the effectiveness of our approach on publicly available datasets, including DDR, IDRiD, and E-Ophtha. Our results demonstrate that DB U-Net achieves AUPR values of 0.5254 and 0.7297 for Microaneurysms and soft exudates segmentation, respectively, on the IDRiD dataset. These results outperform other models, highlighting the potential clinical utility of our method in identifying retinal lesions from retinal fundus images.

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Section: Introductionmentioning

confidence: 99%