Title: Retina blood vessels segmentation by combining deep learning networks

Authors: Mohamed Elssaleh Bachiri; Adel Rahmoune; Fayçal Rahmoune

Addresses: Limose Laboratory, Department of Electrical Engineering, Faculty of Technolgy, University M'hamed Bougara – Boumerdes, Boumerdes, 35000, Algeria ' Limose Laboratory, Department of Electrical Engineering, Faculty of Technolgy, University M'hamed Bougara – Boumerdes, Boumerdes, 35000, Algeria ' Limose Laboratory, Department of Electrical Engineering, Faculty of Technolgy, University M'hamed Bougara – Boumerdes, Boumerdes, 35000, Algeria

Abstract: In this paper, we propose two deep learning architectures for the segmentation and detection of the vascular networks of blood vessels in fundus images. First, we combined VGG16 with U-net, then, we used Resnet 34 in combination with U-net. Both architectures employ an encoding and a decoding path. In this paper, we used the DRIVE and STARE databases. After applying VGG 16+U-net on the DRIVE database, we obtained the accuracy value of 0.96955, 0.79929 sensitivity, 0.98624 specificity, 0.9805 recall, and 0.9833 F1-score. We applied VGG 16+U-net on STARE database and we got 0.95259 accuracy, 0.89996 sensitivity, 0.95530 specificity, 0.9933 recall, and 0.9742 F1-score. Concerning Resnet 34 + U-net, we got the value of 0.9692 accuracy, 0.7859 sensitivity, 0.9870 specificity, 0.9794 recall, and 0.9832 F1-score after applying on DRIVE database. Moreover, we got 0.9363 accuracy, 0.9335 sensitivity, 0.9246 specificity, 0.9961 recall, and 0.9649 F1-score after we applied Resnet 34+U-net on STARE.

Keywords: retinal segmentation; convolution neuron network; U-Net; deep learning; VGG 16; Resnet 34.

DOI: 10.1504/IJBET.2023.133720

International Journal of Biomedical Engineering and Technology, 2023 Vol.43 No.1, pp.38 - 59

Received: 12 Mar 2022
Accepted: 05 Oct 2022
Published online: 02 Oct 2023 *

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