基于机器学习的新冠肺炎后x射线自动识别S-CNN模型

Suchismita Deb, Amiya Dey, Joyjit Patra, Monalisa Chakraborty, Subir Gupta
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引用次数: 0

摘要

COVID-19改变了全球,并在全球蔓延。COVID - 19简化和加快了区域程序。因为这种疾病通过人传播,所以CO VID测试和数据在人类中非常普遍。因此,确定哪些人受到影响至关重要。是时候继续你的生活了。胸部x光和ct扫描是最常用的检测方法。胸部x光检查是最快、最便宜的治疗方法。采用胸片和模型对COVID没有cyclopean振幅测试包。FCNN是一种标准的图像处理算法。该模型应该能够从照片中快速识别CO VID。我们在研究中提出了一个S-CNN模型作为整个CNN的基础。我们开发的模型非常适合任何齿轮系统,并且具有较低的时间复杂度。该方法可以在未知图像中检测出COVID,准确率为92%。该模型为从私人数据估计COVID提供了合理和充分的响应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning-based S-CNN model for automated post-covid X-RAY identification
COVID-19 has transmuted the globe and spread throughout the world. The COVID has streamlined and expedited regional procedures. Because the disease spreads via people, the CO VID test and data are pretty prevalent in humans. It is therefore vital to identify those who are affected. It's time to get on with your life. Chest X-ray and CT-SCAN are the most commonly used CO VID testing procedures. A chest X-ray is the quickest and least expensive treatment. There are no cyclopean amplitude test packets for COVID employing chest X-ray and model. FCNN is a standard image processing algorithm. The model should be able to recognize CO VID from a photo quickly. We proposed an S-CNN model as the foundation for the whole CNN in the study. The model we developed is very adaptable to any gear system and has low temporal complexity. The method can detect COVID in an unknown image with 92 percent accuracy. The model provides a reasonable and adequate response for estimating COVID from private data.
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