@Article{cmc.2023.043239,
AUTHOR = {Abdul Qadir Khan, Guangmin Sun, Yu Li, Anas Bilal, Malik Abdul Manan},
TITLE = {Optimizing Fully Convolutional Encoder-Decoder Network for Segmentation of Diabetic Eye Disease},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {77},
YEAR = {2023},
NUMBER = {2},
PAGES = {2481--2504},
URL = {http://www.techscience.com/cmc/v77n2/54787},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2023.043239}
}