International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 16, No. 4, 8 Aug. 2026
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Ensemble-Based Modelling for Enhanced Detection of Pneumonia Disease
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Author(s)
Index Terms
Pneumonia, X-ray imaging, deep learning, ensemble learning, soft voting
Abstract
Pneumonia is a lung condition that is rather prevalent and has the potential to be lethal. The early diagnosis of this disease is absolutely necessary to cut down on the number of fatalities. The aim of this research is to provide a decision-support tool that can help professionals in the field identify cases of pneumonia. The experimental studies used two publicly available datasets in the Kaggle repository. First, experiments were conducted with the pre-trained models. Then, hard and soft voting ensemble learning approaches were implemented using the five most successful deep learning models. According to the results, the soft voting approach outperformed others, with accuracies of 98.55% and 97.26% in two- and three-class datasets, respectively. With this result, a software included this approach has been developed to assist field experts in their decision-making.
Cite This Paper
Mustafa Oguzhan Ozdemir, Kemal Akyol, "Ensemble-Based Modelling for Enhanced Detection of Pneumonia Disease", International Journal of Engineering and Manufacturing (IJEM), Vol.16, No.4, pp.58-70, 2026. DOI:10.5815/ijem.2026.04.04
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