Binary Data Augmentation Selection using WSO for Diabetic Retinopathy Classification
Nedaa Almansour1,2,*, Azizi Abdullah2, Dheeb Albashish3,4, Shahnorbanun Sahran2,*
1 Autonomous Systems Department, Faculty of Artificial Intelligence, Al-Balqa Applied University, Salt, Jordan
2 Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia
3 Computer Science Department, Al-Ahliyya Amman University, Amman, Jordan
4 Computer Science Department, Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Salt, Jordan
* Corresponding Author: Nedaa Almansour. Email: 
, 
, 
; Shahnorbanun Sahran. Email: 
Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.086450
Received 30 May 2026; Accepted 05 August 2026; Published online 31 August 2026
Abstract
Data augmentation (DA) techniques are widely used in convolutional neural networks (CNNs) to artificially expand the size of training datasets. This is particularly true for medical imaging tasks, such as Diabetic Retinopathy (DR) image classification, where the training data are often limited and imbalanced. Various DA techniques are utilized in CNN models, including horizontal and vertical flipping, rotation, and zoom. Combining distinct methods increases the diversity of the produced images and allows the CNNs to handle the complex details in the images. Manually designed or heuristically selected augmentation combinations may generate redundant or highly similar training samples, which can negatively affect model generalization and classification performance. To analyze the effect of DA combinations on DR classification, the selection of the augmentation subset is formulated as a binary combinatorial optimization problem, in which the recent metaheuristic White Shark Optimizer (WSO) is employed to identify the optimal augmentation subset. Since the original WSO was designed for continuous optimization, the proposed Binary WSO (BWSO) adapts the WSO to binary search spaces through a sigmoid-based binary transfer mechanism. In each iteration of the WSO, candidate augmentation subsets are evaluated using the validation performance of three pre-trained CNN architectures (VGG16, DenseNet121, and MobileNetV2) to identify augmentation combinations that improve classification robustness and generalization. The proposed Adaptive Metaheuristic Data Augmentation–White Shark Optimizer (AMDA-WSO) framework was evaluated on the APTOS 2019 dataset containing 3662 retinal fundus images in five severity grades of DR and compared with Particle Swarm Optimization (PSO) and the Bees Algorithm (BA). Experimental results demonstrated that the AMDA-WSO-guided MobileNetV2 achieved the best performance, reaching 92.08% accuracy and 81.48% sensitivity. These findings confirm the effectiveness of BWSO for automated binary augmentation subset selection in DR image classification.
Keywords
Framework; adaptive data augmentation; metaheuristic optimization; White Shark Optimizer (WSO); pre-trained CNN models; diabetic retinopathy classification
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