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Abstract：
Pulmonary disease identification and characterization are among the most intriguing research topics of recent years since they require an accurate and prompt diagnosis. Although pulmonary radiography has helped in lung disease diagnosis, the interpretation of the radiographic image has always been a major concern for doctors and radiologists to reduce diagnosis errors. Due to their success in image classification and segmentation tasks, cutting-edge artificial intelligence techniques like machine learning (ML) and deep learning (DL) are widely encouraged to be applied in the field of diagnosing lung disorders and identifying them using medical images, particularly radiographic ones. For this end, the researchers are concurring to build systems based on these techniques in particular deep learning ones. In this paper, we proposed three deep-learning models that were trained to identify the presence of certain lung diseases using thoracic radiography. The first model, named \"CovCXR-Net\", identifies the COVID-19 disease (two cases: COVID-19 or normal). The second model, named \"MDCXR3-Net\", identifies the COVID-19 and pneumonia diseases (three cases: COVID-19, pneumonia, or normal), and the last model, named \"MDCXR4-Net\", is destined to identify the COVID-19, pneumonia and the pulmonary opacity diseases (4 cases: COVID-19, pneumonia, pulmonary opacity or normal). These models have proven their superiority in comparison with the state-of-the-art models and reached an accuracy of 99,09 %, 97.74 %, and 90,37 % respectively with three benchmarks.
摘要：
肺部疾病的识别和表征是近年来最有趣的研究课题之一，因为它们需要准确和及时的诊断。尽管肺部X线摄影有助于肺部疾病诊断，放射线图像的解释一直是医生和放射科医生减少诊断错误的主要关注点。由于他们在图像分类和分割任务中的成功，诸如机器学习（ML）和深度学习（DL）之类的尖端人工智能技术被广泛鼓励应用于诊断肺部疾病并使用医学图像识别它们的领域，特别是射线照相的。为此,研究人员同意基于这些技术，特别是深度学习技术来构建系统。在本文中,我们提出了三种深度学习模型,这些模型经过训练,可以使用胸部X线摄影术来识别某些肺部疾病的存在.第一个模型，名为“CovCXR-Net”，确定COVID-19疾病（两例：COVID-19或正常）。第二个模型，名为\"MDCXR3-Net\",识别COVID-19和肺炎疾病(三例：COVID-19，肺炎，或正常)，最后一个模型，名为\"MDCXR4-Net\",注定要识别COVID-19，肺炎和肺部混浊疾病（4例：COVID-19，肺炎，肺混浊或正常）。这些模型与最先进的模型相比已证明了其优越性，并达到了99,09％的准确性，97.74%,三个基准分别为90,37%。
