Diabetic retinopathy (DR) is one of the most common causes of vision loss or blindness globally. Early detection of retinal eye lesions like hard exudates, soft exudates, microaneurysms, and hemorrhages is crucial to detect DR in a human eye. Therefore, accurate segmentation of lesions from eye fundus images is essential to develop efficient automated DR detection systems. This paper presented a novel hard and soft exudates lesions segmentation method called Efficient Dual-Decoder Boosted Network (EDBNet). EDBNet is composed of the following main components: 1) pre-trained ImageNet ResNet50 encoder with Atrous Spatial Pyramid Pooling (ASPP), 2) UNet decoder block with Gated Skip Connections mechanism to enhance capture more details of fundus images, 3) dual-decoder boosted to improve the performance segmentation of retinal lesion in the eye fundus images, and fusion outputs of the dual-decoder boosted to generate enhanced exudates segmentation. The effectiveness of the proposed framework is assessed on the IDRiD publicly dataset in terms of accuracy, Area Under Precision-Recall (AUPR), IOU, and Dice metrics. EDBNet obtains 99.8, 74.4, 78.0, and 87.6% of soft exudates, respectively. For hard exudates, EDBNet achieves 99.5, 85.3, 80.3, and 89.1%, respectively. The experimental results also demonstrate that EDBNet outperforms many state-of-the-art methods.
The optic disc (OD) is the point where the retinal vessels begin. OD carries essential information linked to Diabetic Retinopathy and glaucoma that may cause vision loss. Therefore, accurate segmentation of the optic disc from eye fundus images is essential to develop efficient automated DR and glaucoma detection systems. This paper presents a deep learning-based system for OD segmentation based on an ensemble of efficient semantic segmentation models for medical image segmentation. The aggregation of the different DL models was performed with the ordered weighted averaging (OWA) operators. We proposed the use of a dynamically generated set of weights that can give a different contribution to the models according to their performance during the segmentation of OD in the eye fundus images. The effectiveness of the proposed system was assessed on a fundus image dataset collected from the Hospital Sant Joan de Reus. We obtained Jaccard, Dice, Precision, and Recall scores of 95.40, 95.10, 96.70, and 93.90%, respectively.
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