Using Convolutional Neural Networks for the Classification of Suboptimal Chest Radiographs.

Journal: Journal of medical radiation sciences
Published Date: Jul 18, 2025

Abstract

INTRODUCTION: Chest X-rays (CXR) rank among the most conducted X-ray examinations. They often require repeat imaging due to inadequate quality, leading to increased radiation exposure and delays in patient care and diagnosis. This research assesses the efficacy of DenseNet121 and YOLOv8 neural networks in detecting suboptimal CXRs, which may minimise delays and enhance patient outcomes.

Authors

  • Emily Huanke Liu
    Department of Medical Imaging and Radiation Sciences, Monash University, Clayton, Victoria, Australia.
  • Daniel Carrion
    Monash Imaging, Monash Health, Clayton, Victoria, Australia.
  • Mohamed Khaldoun Badawy
    Department of Medical Imaging and Radiation Sciences, Monash University, Clayton, Victoria, Australia.

Keywords

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