Khan Ali Yousuf, Luque-Nieto Miguel-Angel, Saleem Muhammad Imran, Nava-Baro Enrique
Telecommunications Engineering School, University of Malaga, 29010 Malaga, Spain.
Institute of Oceanic Engineering Research, University of Malaga, 29010 Malaga, Spain.
J Imaging. 2024 Dec 19;10(12):328. doi: 10.3390/jimaging10120328.
On 11 February 2020, the prevalent outbreak of COVID-19, a coronavirus illness, was declared a global pandemic. Since then, nearly seven million people have died and over 765 million confirmed cases of COVID-19 have been reported. The goal of this study is to develop a diagnostic tool for detecting COVID-19 infections more efficiently. Currently, the most widely used method is Reverse Transcription Polymerase Chain Reaction (RT-PCR), a clinical technique for infection identification. However, RT-PCR is expensive, has limited sensitivity, and requires specialized medical expertise. One of the major challenges in the rapid diagnosis of COVID-19 is the need for reliable imaging, particularly X-ray imaging. This work takes advantage of artificial intelligence (AI) techniques to enhance diagnostic accuracy by automating the detection of COVID-19 infections from chest X-ray (CXR) images. We obtained and analyzed CXR images from the Kaggle public database (4035 images in total), including cases of COVID-19, viral pneumonia, pulmonary opacity, and healthy controls. By integrating advanced techniques with transfer learning from pre-trained convolutional neural networks (CNNs), specifically InceptionV3, ResNet50, and Xception, we achieved an accuracy of 95%, significantly higher than the 85.5% achieved with ResNet50 alone. Additionally, our proposed method, CXR-DNNs, can accurately distinguish between three different types of chest X-ray images for the first time. This computer-assisted diagnostic tool has the potential to significantly enhance the speed and accuracy of COVID-19 diagnoses.
2020年2月11日，一种冠状病毒疾病——新冠病毒肺炎的广泛爆发被宣布为全球大流行。自那时以来，已有近700万人死亡，报告的新冠病毒肺炎确诊病例超过7.65亿例。本研究的目的是开发一种更高效检测新冠病毒肺炎感染的诊断工具。目前，最广泛使用的方法是逆转录聚合酶链反应（RT-PCR），这是一种用于感染识别的临床技术。然而，RT-PCR成本高昂、灵敏度有限，且需要专业医学知识。新冠病毒肺炎快速诊断的主要挑战之一是需要可靠的成像，尤其是X射线成像。这项工作利用人工智能（AI）技术，通过自动从胸部X线（CXR）图像中检测新冠病毒肺炎感染来提高诊断准确性。我们从Kaggle公共数据库获取并分析了CXR图像（总共4035张图像），包括新冠病毒肺炎、病毒性肺炎、肺部混浊和健康对照的病例。通过将先进技术与来自预训练卷积神经网络（CNN）的迁移学习相结合，特别是InceptionV3、ResNet50和Xception，我们实现了95%的准确率，显著高于仅使用ResNet50时达到的85.5%。此外，我们提出的方法CXR-DNNs首次能够准确区分三种不同类型的胸部X线图像。这种计算机辅助诊断工具有可能显著提高新冠病毒肺炎诊断的速度和准确性。
