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Optimizing Fully Convolutional Encoder-Decoder Network for Segmentation of Diabetic Eye Disease

Abdul Qadir Khan1, Guangmin Sun1,*, Yu Li1, Anas Bilal2, Malik Abdul Manan1

1 Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
2 College of Information Science Technology, Hainan Normal University, Haikou, 571158, China

* Corresponding Author: Guangmin Sun. Email: email

(This article belongs to this Special Issue: Recent Advances in Ophthalmic Diseases Diagnosis using AI)

Computers, Materials & Continua 2023, 77(2), 2481-2504. https://doi.org/10.32604/cmc.2023.043239

Abstract

In the emerging field of image segmentation, Fully Convolutional Networks (FCNs) have recently become prominent. However, their effectiveness is intimately linked with the correct selection and fine-tuning of hyperparameters, which can often be a cumbersome manual task. The main aim of this study is to propose a more efficient, less labour-intensive approach to hyperparameter optimization in FCNs for segmenting fundus images. To this end, our research introduces a hyperparameter-optimized Fully Convolutional Encoder-Decoder Network (FCEDN). The optimization is handled by a novel Genetic Grey Wolf Optimization (G-GWO) algorithm. This algorithm employs the Genetic Algorithm (GA) to generate a diverse set of initial positions. It leverages Grey Wolf Optimization (GWO) to fine-tune these positions within the discrete search space. Testing on the Indian Diabetic Retinopathy Image Dataset (IDRiD), Diabetic Retinopathy, Hypertension, Age-related macular degeneration and Glacuoma ImageS (DR-HAGIS), and Ocular Disease Intelligent Recognition (ODIR) datasets showed that the G-GWO method outperformed four other variants of GWO, GA, and PSO-based hyperparameter optimization techniques. The proposed model achieved impressive segmentation results, with accuracy rates of 98.5% for IDRiD, 98.7% for DR-HAGIS, and 98.4%, 98.8%, and 98.5% for different sub-datasets within ODIR. These results suggest that the proposed hyperparameter-optimized FCEDN model, driven by the G-GWO algorithm, is more efficient than recent deep-learning models for image segmentation tasks. It thereby presents the potential for increased automation and accuracy in the segmentation of fundus images, mitigating the need for extensive manual hyperparameter adjustments.

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Cite This Article

A. Q. Khan, G. Sun, Y. Li, A. Bilal and M. A. Manan, "Optimizing fully convolutional encoder-decoder network for segmentation of diabetic eye disease," Computers, Materials & Continua, vol. 77, no.2, pp. 2481–2504, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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