Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
Department of Systemics, School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India.
Contrast Media Mol Imaging. 2022 Feb 28;2022:8549707. doi: 10.1155/2022/8549707. eCollection 2022.
Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 emerged in China and quickly spread around the globe, resulting in the coronavirus epidemic of 2019-22. As this virus is very similar to influenza in its early stages, its accurate detection is challenging. Several techniques for detecting the virus in its early stages are being developed. Deep learning techniques are a handy tool for detecting various diseases. For the classification of COVID-19 and influenza, we proposed tailored deep learning models. A publicly available dataset of X-ray images was used to develop proposed models. According to test results, deep learning models can accurately diagnose normal, influenza, and COVID-19 cases. Our proposed long short-term memory (LSTM) technique outperformed the CNN model in the evaluation phase on chest X-ray images, achieving 98% accuracy.
冠状病毒（COVID-19）是一种致命病毒，最初表现为类似流感的症状。COVID-19 最初出现在中国，并迅速在全球范围内传播，导致了 2019-22 年的冠状病毒疫情。由于这种病毒在早期阶段与流感非常相似，因此准确检测具有挑战性。目前正在开发几种用于早期检测病毒的技术。深度学习技术是检测各种疾病的便捷工具。对于 COVID-19 和流感的分类，我们提出了定制的深度学习模型。使用公开的 X 射线图像数据集来开发提出的模型。根据测试结果，深度学习模型可以准确诊断正常、流感和 COVID-19 病例。在胸部 X 射线图像的评估阶段，我们提出的长短期记忆（LSTM）技术优于 CNN 模型，准确率达到 98%。
