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EDR-Net: Lightweight deep neural network architecture for detecting referable diabetic retinopathy

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...

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Main Authors: Aujih, Ahmad Bukhari, Shapiai, Mohd. Ibrahim, Meriaudeau, Fabrice, Tang, Tong Boon
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/104439/
http://dx.doi.org/10.1109/TBCAS.2022.3182907
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spelling my.utm.1044392024-02-04T10:04:26Z http://eprints.utm.my/104439/ EDR-Net: Lightweight deep neural network architecture for detecting referable diabetic retinopathy Aujih, Ahmad Bukhari Shapiai, Mohd. Ibrahim Meriaudeau, Fabrice Tang, Tong Boon T Technology (General) 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. Institute of Electrical and Electronics Engineers Inc. 2022 Article PeerReviewed Aujih, Ahmad Bukhari and Shapiai, Mohd. Ibrahim and Meriaudeau, Fabrice and Tang, Tong Boon (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 1932-4545 http://dx.doi.org/10.1109/TBCAS.2022.3182907 DOI : 10.1109/TBCAS.2022.3182907
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic T Technology (General)
spellingShingle T Technology (General)
Aujih, Ahmad Bukhari
Shapiai, Mohd. Ibrahim
Meriaudeau, Fabrice
Tang, Tong Boon
EDR-Net: Lightweight deep neural network architecture for detecting referable diabetic retinopathy
description 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.
format Article
author Aujih, Ahmad Bukhari
Shapiai, Mohd. Ibrahim
Meriaudeau, Fabrice
Tang, Tong Boon
author_facet Aujih, Ahmad Bukhari
Shapiai, Mohd. Ibrahim
Meriaudeau, Fabrice
Tang, Tong Boon
author_sort Aujih, Ahmad Bukhari
title EDR-Net: Lightweight deep neural network architecture for detecting referable diabetic retinopathy
title_short EDR-Net: Lightweight deep neural network architecture for detecting referable diabetic retinopathy
title_full EDR-Net: Lightweight deep neural network architecture for detecting referable diabetic retinopathy
title_fullStr EDR-Net: Lightweight deep neural network architecture for detecting referable diabetic retinopathy
title_full_unstemmed EDR-Net: Lightweight deep neural network architecture for detecting referable diabetic retinopathy
title_sort edr-net: lightweight deep neural network architecture for detecting referable diabetic retinopathy
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2022
url http://eprints.utm.my/104439/
http://dx.doi.org/10.1109/TBCAS.2022.3182907
_version_ 1792147741281878016
score 13.211869

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