Department of Computer Science and Engineering, KIET Group of Institutions, Ghaziabad 201206, India.
Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 12435, Saudi Arabia.
Sensors (Basel). 2022 Oct 2;22(19):7474. doi: 10.3390/s22197474.
Academics and the health community are paying much attention to developing smart remote patient monitoring, sensors, and healthcare technology. For the analysis of medical scans, various studies integrate sophisticated deep learning strategies. A smart monitoring system is needed as a proactive diagnostic solution that may be employed in an epidemiological scenario such as COVID-19. Consequently, this work offers an intelligent medicare system that is an IoT-empowered, deep learning-based decision support system (DSS) for the automated detection and categorization of infectious diseases (COVID-19 and pneumothorax). The proposed DSS system was evaluated using three independent standard-based chest X-ray scans. The suggested DSS predictor has been used to identify and classify areas on whole X-ray scans with abnormalities thought to be attributable to COVID-19, reaching an identification and classification accuracy rate of 89.58% for normal images and 89.13% for COVID-19 and pneumothorax. With the suggested DSS system, a judgment depending on individual chest X-ray scans may be made in approximately 0.01 s. As a result, the DSS system described in this study can forecast at a pace of 95 frames per second (FPS) for both models, which is near to real-time.
学术界和医疗界非常关注开发智能远程患者监测、传感器和医疗技术。为了分析医学扫描，各种研究都集成了复杂的深度学习策略。需要一个智能监测系统作为主动诊断解决方案，可用于 COVID-19 等流行病学情况。因此，这项工作提供了一个智能医疗保险系统，这是一个基于物联网的深度学习决策支持系统 (DSS)，用于自动检测和分类传染病（COVID-19 和气胸）。该建议的 DSS 系统使用三个独立的基于标准的胸部 X 射线扫描进行了评估。所建议的 DSS 预测器已用于识别和分类整个 X 射线扫描中存在的异常区域，认为这些异常区域归因于 COVID-19，对正常图像的识别和分类准确率为 89.58%，对 COVID-19 和气胸的识别和分类准确率为 89.13%。使用建议的 DSS 系统，大约可以在 0.01 秒内根据个人胸部 X 射线扫描做出判断。因此，本研究中描述的 DSS 系统可以为两个模型以每秒 95 帧的速度进行预测，接近实时。
