DOI: 10.29130/dubited.1845632 ISSN: 2148-2446

The Effectiveness of Data Augmentation in Image Classification Using Whale Optimization Algorithm

Ömer Özcan, Muhammed Karaaltun, Mehmet Hacıbeyoglu
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Imbalanced datasets significantly degrade the performance of image classification systems, particularly in application domains where data collection is difficult, such as medical diagnosis and plant disease analysis. Data augmentation is a commonly used strategy to alleviate this problem by increasing the number of samples in minority classes. In this study, a novel image augmentation approach based on optimized edge detection, named Whale Optimization Algorithm–based Image Augmentation (WOA-IA), is proposed. Unlike conventional augmentation techniques, the proposed method generates synthetic samples by combining original images with optimally extracted edge representations. Edge detection is performed using a multi-layer filter structure whose parameters are optimized via the whale optimization algorithm by maximizing similarity with reference edge images. The resulting edge images are then integrated into an angular placement-based augmentation model. The effectiveness of the proposed WOA-IA method is evaluated on five widely used imbalanced datasets—Plant Leaves, Flowers102, Cassava, Plant Village, and HAM10000—using pre-trained CNN models including DenseNet201, MobileNet, and VGG16. Experimental results demonstrate that the proposed method consistently improves classification performance compared to both imbalanced datasets and standard edge-based augmentation techniques, particularly for datasets with high imbalance ratios.

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