Pneumonia is one of the very common life-threatening diseases and needs proper diagnosis at an early stage to be cured expeditiously. Medical practitioners use chest X-ray as the best imaging modality to identify pneumonia. Due to the limited facilities available at the remote places and the need of maintaining the social distancing imposed by the recent outbreak of coronavirus disease, one may not have ease of access to a professional radiologist. This article proposes a deep learning (DL) framework that detects pneumonia from X-ray images to assist the medical practitioners located at distant places. The X-ray images are captured as compressed sensing (CS) measurements i.e. very few numbers of samples are observed in order to obtain an energy efficient and bandwidth preserving system to be utilized for far-end pneumonia detection purpose. Extensive simulation results show that the proposed approach enables the detection of pneumonia with 96.48% accuracy when only 30% samples are transmitted.

中文翻译：

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肺炎检测中压缩感知测量的深度学习

肺炎是一种非常常见的危及生命的疾病，需要早期正确诊断才能迅速治愈。医生使用胸部 X 射线作为识别肺炎的最佳成像方式。由于偏远地区可用的设施有限，以及最近爆发的冠状病毒病需要保持社交距离，因此可能无法轻松接触到专业的放射科医生。本文提出了一种深度学习 (DL) 框架，该框架可以从 X 射线图像中检测肺炎，以帮助位于远方的医生。X 射线图像被捕获为压缩传感 (CS) 测量，即观察到的样本数量非常少，以获得用于远端肺炎检测目的的节能和带宽保持系统。