Utilization of a Deep Convolutional Neural Network for the Binary Classification of Chest X-Ray Pneumonia
Received: 30 November 2024 | Revised: 25 December 2024 | Accepted: 1 January 2025 | Online: 26 January 2025
Corresponding author: Ibraheem Kasim Ibraheem
Abstract
Pneumonia remains a significant global health concern, necessitating efficient diagnostic tools. This study presents a novel Convolutional Neural Network (CNN) architecture, CuDenseNet, designed for the binary classification of Chest X-Ray (CXR) images as either having pneumonia or normal (healthy). Unlike models that rely on transfer learning from pre-trained architectures, CuDenseNet is trained from scratch and incorporates three parallel DenseNet paths of varying depths, enhancing feature extraction and classification accuracy. The model was evaluated on a combined dataset of 11,708 CXR images, achieving exceptional performance metrics of 99.1% accuracy, 99.7% precision, 99.1% recall, and an AUC of 99.7%. The comparative analysis demonstrates that CuDenseNet outperforms state-of-the-art pre-trained models such as VGG19 and ResNet50 while providing superior adaptability. These results underscore the potential of CuDenseNet as a robust and reliable tool for automated pneumonia diagnosis, with significant implications for clinical applications and future research in medical imaging.
Keywords:
deep learning, convolutional neural networks, chest X-ray images, pneumonia classification, image processing, Computer-Aided Diagnosis (CAD), health informatics, AIDownloads
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Copyright (c) 2025 Hyder Nataq Taha Al-Azzawi, Ahmad Ghandour, Haider Ali, Ahmad Taher Azar, Najla Althuniyan, Ibraheem Kasim Ibraheem, Yousif I. Hammadi, Amjad J. Humaidi, Zeeshan Haider, Saim Ahmed
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