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Mathematical and Computational Data Modeling
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Data Modeling

Volume 6 Number 1 2025

Article Contents

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RESEARCH ARTICLE   (Open Access)
Contents Vol 6 (1)

Enhancing Pneumonia Detection from Chest X-Ray Images Using Integrated Preprocessing and Deep Learning Techniques

Md. Ataur Rahman1*, Kamruzzaman Mithu 1, Sayed Rokibul Hossain1, Md. Nesar Uddin1, Khondaker Abdullah Al Mamun1

+ Author Affiliations

Data Modeling 6 (1) 1-8 https://doi.org/10.25163/data.6110746

Submitted: 28 January 2025 Revised: 09 April 2025  Published: 14 April 2025 

Abstract

Pneumonia continues to pose a significant global health burden, particularly in regions where access to expert radiological interpretation remains limited. While chest X-ray imaging is widely used for diagnosis, its interpretation can be time-consuming and, at times, subject to variability. In this context, machine learning—especially deep learning—has emerged as a promising tool for automated detection. Yet, the extent to which preprocessing strategies influence model performance is not always fully appreciated. This study explores a structured approach to pneumonia detection using chest X-ray images, with a particular emphasis on preprocessing techniques. The analysis was conducted on the JSRT dataset, incorporating multiple stages of refinement, including bone shadow exclusion, lung segmentation, and outlier removal using t-distributed stochastic neighbor embedding (t-SNE). A convolutional neural network (CNN), based on the VGG16 architecture, was employed for classification. Training and validation were performed using a GPU-enabled TensorFlow environment, with performance evaluated across multiple dataset configurations. The results suggest that preprocessing plays a decisive role in shaping model outcomes. The fully processed dataset—combining segmentation, artifact removal, and dimensionality refinement—yielded the highest accuracy (approximately 0.71), whereas less refined datasets performed comparatively lower. However, a divergence between training and validation accuracy indicates the presence of overfitting, underscoring challenges in generalization. Taken together, the findings suggest that while deep learning models are capable, their effectiveness depends strongly on data quality and preprocessing design. This work contributes to the ongoing effort to develop more reliable, accessible diagnostic tools, particularly for resource-constrained clinical settings.

Keywords: Pneumonia Detection; Chest X-Ray; Deep Learning; Image Preprocessing; Convolutional Neural Network

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