Automated Pneumonia Detection from Chest X-rays Using a ResNet-34 Architecture

Corresponding author: liuxinkui, lxkarticle@126.com

DOI: 10.12201/bmr.202608.00019

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  Abstract: Objective/Significance Aiming at the problems of low efficiency and strong subjectivity in the diagnosis of pneumonia on chest X-ray images, this study proposes a deep learning classification method based on resnet34. It can significantly improve the diagnostic efficiency, reduce the misdiagnosis rate, and provide objective and reliable auxiliary support for clinical decision-making. Method/Process In this study, a hybrid global local feature enhancement module (HGFE) was proposed to extract local features and global context information through a double branch structure. The local branch used hole convolution to expand the receptive field, the global branch introduced coordinate attention mechanism to establish long-range dependence, and realized feature fusion through se module. Results/Conclusions Experiments on the chestx-ray2017 dataset showed that resnet34 HGFE model achieves 98.16% accuracy, 0.9758 F1 score and 0.9896 AUC value, which was significantly better than vgg16 and resnet50 models. The resnet34 model proposed in this study effectively improved the detection performance of pneumonia lesions. This study provided an effective solution for the automatic and high-precision diagnosis of pneumonia, and had important clinical practical value. The automatic classification method based on RESNET will show greater application potential. Future research will further optimize the model, improve the classification speed and accuracy, and enhance the practical value of this method in medical image analysis.

  Key words: ResNet34-HGFE, Pneumonia, Medical image classification

  Submit time: 7 August 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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