eprintid: 16667
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/66/67
datestamp: 2023-12-19 03:23:11
lastmod: 2023-12-19 03:23:11
status_changed: 2023-12-19 03:06:40
type: article
metadata_visibility: show
creators_name: Aujih, A.B.
creators_name: Shapiai, M.I.
creators_name: Meriaudeau, F.
creators_name: Tang, T.B.
title: EDR-Net: Lightweight Deep Neural Network Architecture for Detecting Referable Diabetic Retinopathy
ispublished: pub
keywords: Computational efficiency; Computer architecture; Convolution; Eye protection; Network architecture; Statistical tests, Deep learning; Diabetic retinopathy; Diabetic retinopathy classification; Efficient convolution; Lightweight deep neural network depth-wise separable convolution; NET architecture; Network depths; Neural-networks; Retina, Deep neural networks, algorithm; diabetes mellitus; diabetic retinopathy; diagnostic imaging; human; receiver operating characteristic, Algorithms; Diabetes Mellitus; Diabetic Retinopathy; Humans; Neural Networks, Computer; ROC Curve
note: cited By 6
abstract: Present architecture of convolution neural network for diabetic retinopathy (DR-Net) is based on normal convolution (NC). It incurs high computational cost as NC uses a multiplicative weight that measures a combined correlation in both cross-channel and spatial dimension of layer's inputs. This might cause the overall DR-Net architecture to be over-parameterised and computationally inefficient. This paper proposes EDR-Net-a new end-to-end, DR-Net architecture with depth-wise separable convolution module. The EDR-Net architecture was trained with DRKaggle-train dataset (35,126 images), and tested on two datasets, i.e. DRKaggle-test (53,576 images) and Messidor-2 (1,748 images). Results showed that the proposed EDR-Net achieved predictive performance comparable with current state-of-the-arts in detecting referable diabetic retinopathy (rDR) from fundus images and outperformed other light weight architectures, with at least two times less computation cost. This makes it more amenable for mobile device based computer-assisted rDR screening applications. © 2007-2012 IEEE.
date: 2022
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132695820&doi=10.1109%2fTBCAS.2022.3182907&partnerID=40&md5=2f47cbd2430b2c0bfdb893d962b85891
id_number: 10.1109/TBCAS.2022.3182907
full_text_status: none
publication: IEEE Transactions on Biomedical Circuits and Systems
volume: 16
number: 3
pagerange: 467-478
refereed: TRUE
issn: 19324545
citation:   Aujih, A.B. and Shapiai, M.I. and Meriaudeau, F. and Tang, T.B.  (2022) EDR-Net: Lightweight Deep Neural Network Architecture for Detecting Referable Diabetic Retinopathy.  IEEE Transactions on Biomedical Circuits and Systems, 16 (3).  pp. 467-478.  ISSN 19324545