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Communication Dans Un Congrès Année : 2023

Deep Learning Architecture with an Optimized Convolutional Processing for the Segmentation of Retinal Blood Vessels

Résumé

The retinal vascular tree (RVT) is crucial for the diagnosis of various ophthalmological diseases. Efficient segmentation of the RVT with reduced runtime is essential for clinical purposes. Recently, convolutional neural networks (CNNs) have been used for RVT segmentation. However, these architectures typically apply fixed and standard size of convolution kernels for all blocks, which may be unsuitable for accurately capturing vessel scales. In addition, these kernels are applied using 3D convolution layers across all channel depths, leading to higher computational complexity. In this work, we propose a novel deep learning architecture. The main contribution consists of performing a convolution processing where kernel size is chosen with respect to vessel scale variation, in order to enhance the quality of the segmentation of vascular trees. In addition, the convolution processing is insured through several layers with 2D kernels, to reduce the computational complexity. The proposed architecture is evaluated on DRIVE database reaching an average accuracy and sensitivity respectively in the order of 97.69% and 91.69% in 0.75 second per fundus image.
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Dates et versions

hal-04451186 , version 1 (11-02-2024)

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Henda Boudegga, Yaroub Elloumi, Rostom Kachouri, Asma Ben Abdallah, Mohamed Hedi Bedoui. Deep Learning Architecture with an Optimized Convolutional Processing for the Segmentation of Retinal Blood Vessels. 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT), Jul 2023, Rome, Italy. pp.2451-2456, ⟨10.1109/CoDIT58514.2023.10284289⟩. ⟨hal-04451186⟩
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