(Computer Sciences Department, Badji Mokhtar University of Annaba, Annaba, Algeria)(2) Mohammed Tarek Khadir
(Computer Sciences Department, Badji Mokhtar University of Annaba, Annaba, Algeria)(3) Andri Pranolo
(Department of Informatics, Universitas Ahmad Dahlan, Yogyakarta, Indonesia)(4) Modawy Adam Ali Abdalla
(Department of Electrical and Electronic Engineering, Nyala University, Nyala, Sudan)*corresponding author
AbstractChest X-ray (CXR) classification tasks often suffer from severe class imbalance, resulting in biased predictions and suboptimal diagnostic performance. To address this challenge, we propose an integrated framework that combines high-fidelity data augmentation using Generative Adversarial Networks (GANs), ensemble learning via hard and soft voting, and multimodal feature fusion. The method begins by partitioning the majority class into multiple subsets, which are individually balanced through GAN-generated synthetic images. Deep learning models, specifically DenseNet201 and EfficientNetV2B3, are trained separately on each balanced subset. These models are then combined using ensemble voting to improve robustness. Additionally, features extracted from the most performant models are fused and used to train traditional classifiers such as Logistic Regression, Multilayer Perceptron, CatBoost, and XGBoost. Evaluations on a publicly available CXR dataset demonstrate consistent improvements across key metrics, including accuracy, precision, recall, F1-score, AUROC, AUPRC, MCC, and G-mean. This framework shows superior performance in multiclass scenarios.
KeywordsClass Imbalanced; GAN-based Augmentation; Multimodal Fusion; Multiclass CXR Classification; Ensemble Learning.
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DOIhttps://doi.org/10.26555/ijain.v11i3.2092 |
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