The development of deep learning and its growing application in medical diagnosis have focused the attention on automatic control of image quality for neural-network medical image analysis algorithms. This article presents a method for automatic determination of the hardness (penetration) of lung X-ray images using standard criteria from chest X-ray diagnosis. The proposed method can be applied to automatically filter images by hardness (penetration) level and to detect low-quality images, thus facilitating the creation of high-quality data sets and increasing the efficiency of neural-network approaches to the analysis of lung X-ray images.
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Translated from Prikladnaya Matematika i Informatika, No. 67, 2021, pp. 19–30.
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Dovganich, A.A., Khvostikov, A.V., Krylov, A.S. et al. Automatic Quality Control in Lung X-Ray Imaging with Deep Learning. Comput Math Model 32, 276–285 (2021). https://doi.org/10.1007/s10598-021-09539-6
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DOI: https://doi.org/10.1007/s10598-021-09539-6