Utilization of a Deep Convolutional Neural Network for the Binary Classification of Chest X-Ray Pneumonia Article Swipe

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Convolutional neural network Artificial intelligence Pneumonia Computer science Binary classification Deep learning Feature extraction Transfer of learning Recall Pattern recognition (psychology) Precision and recall Feature (linguistics) Machine learning Medicine Support vector machine Psychology Internal medicine Cognitive psychology Linguistics Philosophy

Haydr Nataq Taha Al-Azzawi , Ahmad Ghandour , Ali Haider , Ahmad Taher Azar , Najla Althuniyan , Ibraheem Kasim Ibraheem , Yousif I. Hammadi , Amjad J. Humaidi , Zeeshan Haider , Saim Ahmed ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48084/etasr.9788 · OA: W4407079320

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.