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<p>Pneumonia is a particularly serious lung condition that can be caused by bacteria, viruses, or fungus. Pus and other fluids are deposited in the air sacs of the lungs as a result of this sickness. This illness presents a substantial worldwide health problem, specifically impacting young children and populations that are susceptible. Timely identification and prompt medical intervention are essential in order to avoid serious complications and loss of life. Out of all the diagnostic tools available, chest X-rays are among the most frequently utilized to detect pneumonia. This study introduces an enhanced deep learning framework for pneumonia detection, integrating the Convolutional Block Attention Module (CBAM) with DenseNet-121 to optimize feature extraction through channel and spatial attention. Utilizing the Kermany dataset, comprising 5856 chest X-ray images, the study achieves a test accuracy of 95.57% with baseline DenseNet-121, improving to 98.64% with CBAM integration on a 1056-image test set. The model’s decision-making process was made more understandable by using Grad-CAM visualization, which highlighted the key parts of the lungs where pneumonia patterns were discovered. Models were assessed using a range of metrics, such as accuracy, precision, F1-score, recall, and area under the ROC curve (AUC). On top of that, we benchmarked our proposed architecture against the most advanced pneumonia detection models currently available. Our DenseNet-121 with CBAM achieved better results than other improved models, including complex ensembles, with a far lower number of parameters.</p>

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An Enhanced Deep Learning Framework for Pneumonia Detection in Chest X-rays

  • 期刊论文

  • 发表日期：

    2025年5月15日

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  • Asma Binte Rashid,
  • Jannatul Asma,
  • Krittika Barua,
  • Dhrubajyoti Das

摘要

Pneumonia is a particularly serious lung condition that can be caused by bacteria, viruses, or fungus. Pus and other fluids are deposited in the air sacs of the lungs as a result of this sickness. This illness presents a substantial worldwide health problem, specifically impacting young children and populations that are susceptible. Timely identification and prompt medical intervention are essential in order to avoid serious complications and loss of life. Out of all the diagnostic tools available, chest X-rays are among the most frequently utilized to detect pneumonia. This study introduces an enhanced deep learning framework for pneumonia detection, integrating the Convolutional Block Attention Module (CBAM) with DenseNet-121 to optimize feature extraction through channel and spatial attention. Utilizing the Kermany dataset, comprising 5856 chest X-ray images, the study achieves a test accuracy of 95.57% with baseline DenseNet-121, improving to 98.64% with CBAM integration on a 1056-image test set. The model’s decision-making process was made more understandable by using Grad-CAM visualization, which highlighted the key parts of the lungs where pneumonia patterns were discovered. Models were assessed using a range of metrics, such as accuracy, precision, F1-score, recall, and area under the ROC curve (AUC). On top of that, we benchmarked our proposed architecture against the most advanced pneumonia detection models currently available. Our DenseNet-121 with CBAM achieved better results than other improved models, including complex ensembles, with a far lower number of parameters.