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

Ömer Özcan; Muhammed Karaaltun; Mehmet Hacıbeyoglu

doi:10.29130/dubited.1845632

TR EN

Abstract

Dengesiz veri kümeleri, özellikle tıbbi tanı ve bitki hastalıkları analizi gibi veri toplamanın zor olduğu uygulama alanlarında, görüntü sınıflandırma sistemlerinin performansını önemli ölçüde düşürmektedir. Bu sorunu hafifletmek için azınlık sınıflarındaki örnek sayısını artıran veri artırma (data augmentation) yöntemleri yaygın olarak kullanılmaktadır. Bu çalışmada, Whale Optimization Algorithm–based Image Augmentation (WOA-IA) adı verilen, optimize edilmiş kenar tespitine dayalı yeni bir görüntü artırma yaklaşımı önerilmektedir. Geleneksel artırma tekniklerinden farklı olarak, önerilen yöntem, orijinal görüntüleri en uygun şekilde çıkarılmış kenar temsilleriyle birleştirerek sentetik örnekler üretmektedir. Kenar tespiti, parametreleri referans kenar görüntüleriyle benzerliği maksimize edecek şekilde balina optimizasyon algoritması kullanılarak optimize edilen çok katmanlı bir filtre yapısı aracılığıyla gerçekleştirilmektedir. Elde edilen kenar görüntüleri daha sonra açısal yerleştirmeye dayalı bir artırma modeline entegre edilmektedir. Önerilen WOA-IA yönteminin etkinliği, Plant Leaves, Flowers102, Cassava, Plant Village ve HAM10000 olmak üzere yaygın olarak kullanılan beş dengesiz veri kümesi üzerinde; DenseNet201, MobileNet ve VGG16 gibi önceden eğitilmiş CNN modelleri kullanılarak değerlendirilmiştir. Deneysel sonuçlar, özellikle yüksek dengesizlik oranına sahip veri kümelerinde, önerilen yöntemin hem dengesiz veri kümelerine hem de standart kenar tabanlı artırma tekniklerine kıyasla sınıflandırma performansını tutarlı bir şekilde iyileştirdiğini göstermektedir.

Keywords

Görüntü artırma, kenar tespiti, balina optimizasyon algoritması, dengesiz veri kümeleri.

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

Abstract

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.

Keywords

Image augmentation, Edge detection, Whale optimization algorithm, Imbalanced datasets

Supporting Institution

This research received no external funding.

Ethical Statement

This study does not involve human or animal participants. All procedures followed scientific and ethical principles, and all referenced studies are appropriately cited.

Thanks

The authors do not wish to acknowledge any individual or institution.

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Details

Primary Language

English

Subjects

Deep Learning, Neural Networks, Electrical Energy Generation (Incl. Renewables, Excl. Photovoltaics)

Journal Section

Research Article

Authors

Ömer Özcan ^*
0000-0003-4122-4329
Türkiye

Muhammed Karaaltun
0000-0002-6093-6105
Türkiye

Mehmet Hacıbeyoglu
0000-0003-1830-8516
Türkiye

Publication Date

October 23, 2026

Submission Date

December 19, 2025

Acceptance Date

August 17, 2026

Published in Issue

Year 2026 Volume: 14 Number: 4

DOI

https://doi.org/10.29130/dubited.1845632

IZ

https://izlik.org/JA56TT64GR

APA

Özcan, Ö., Karaaltun, M., & Hacıbeyoglu, M. (2026). The Effectiveness of Data Augmentation in Image Classification Using Whale Optimization Algorithm. Duzce University Journal of Science and Technology, 14(4), 1111-1123. https://doi.org/10.29130/dubited.1845632

AMA

1.Özcan Ö, Karaaltun M, Hacıbeyoglu M. The Effectiveness of Data Augmentation in Image Classification Using Whale Optimization Algorithm. DUBİTED. 2026;14(4):1111-1123. doi:10.29130/dubited.1845632

Chicago

Özcan, Ömer, Muhammed Karaaltun, and Mehmet Hacıbeyoglu. 2026. “The Effectiveness of Data Augmentation in Image Classification Using Whale Optimization Algorithm”. Duzce University Journal of Science and Technology 14 (4): 1111-23. https://doi.org/10.29130/dubited.1845632.

EndNote

Özcan Ö, Karaaltun M, Hacıbeyoglu M (October 1, 2026) The Effectiveness of Data Augmentation in Image Classification Using Whale Optimization Algorithm. Duzce University Journal of Science and Technology 14 4 1111–1123.

IEEE

[1]Ö. Özcan, M. Karaaltun, and M. Hacıbeyoglu, “The Effectiveness of Data Augmentation in Image Classification Using Whale Optimization Algorithm”, DUBİTED, vol. 14, no. 4, pp. 1111–1123, Oct. 2026, doi: 10.29130/dubited.1845632.

ISNAD

Özcan, Ömer - Karaaltun, Muhammed - Hacıbeyoglu, Mehmet. “The Effectiveness of Data Augmentation in Image Classification Using Whale Optimization Algorithm”. Duzce University Journal of Science and Technology 14/4 (October 1, 2026): 1111-1123. https://doi.org/10.29130/dubited.1845632.

JAMA

1.Özcan Ö, Karaaltun M, Hacıbeyoglu M. The Effectiveness of Data Augmentation in Image Classification Using Whale Optimization Algorithm. DUBİTED. 2026;14:1111–1123.

MLA

Özcan, Ömer, et al. “The Effectiveness of Data Augmentation in Image Classification Using Whale Optimization Algorithm”. Duzce University Journal of Science and Technology, vol. 14, no. 4, Oct. 2026, pp. 1111-23, doi:10.29130/dubited.1845632.

Vancouver

1.Ömer Özcan, Muhammed Karaaltun, Mehmet Hacıbeyoglu. The Effectiveness of Data Augmentation in Image Classification Using Whale Optimization Algorithm. DUBİTED. 2026 Oct. 1;14(4):1111-23. doi:10.29130/dubited.1845632

Abstract

Keywords

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

Abstract

Keywords

Supporting Institution

Ethical Statement

Thanks

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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