利用深度神经网络诊断 X 射线图像中的 COVID-19

Mohammed Akram Younus Alsaati
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引用次数: 0

摘要

全球 COVID-19 大流行带来了前所未有的挑战,尤其是检测试剂盒供应有限,阻碍了及时准确的疾病诊断。肺炎是一种常见的 COVID-19 后果,快速识别肺炎对有效管理至关重要。本研究重点关注胸部 X 光图像中的 COVID-19 分类,采用了一种创新方法:通过 Segmentation_Models 软件包将 Xception 模型适配到 U-Net 架构中。U-Net 架构是 CNN 的变体,利用深度学习和图像分割技术,证明非常适合这项任务,尤其是在为分类定制了输出层之后。通过利用 Xception 模型,我们旨在提高 COVID-19 分类的准确性和效率。研究结果表明,COVID-19 病例的自主识别前景广阔,可为医疗保健专业人员提供有价值的支持。医学影像数据与先进神经网络架构的融合为提高大流行病期间的诊断准确性提供了新途径。值得注意的是,报告了每个类别的精确度、召回率和 F1 分数:正常(精确度 = 0.98,召回 = 0.9608,F1 分数 = 0.9704)、肺炎(精确度 = 0.9579,召回 = 0.9579,F1 分数 = 0.9579)和 COVID-19(精确度 = 0.96,召回 = 0.9796,F1 分数 = 0.9698)。这些研究结果表明,我们的方法能够从胸部 X 光图像中准确地对 COVID-19 病例进行分类,为在大流行期间提高诊断能力提供了可行的途径。
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Diagnosis of COVID-19 in X-ray Images using Deep Neural Networks
The global COVID-19 pandemic has presented unprecedented challenges, notably the limited availability of test kits, hindering timely and accurate disease diagnosis. Rapid identification of pneumonia, a common COVID-19 consequence, is crucial for effective management. This study focuses on COVID-19 classification from Chest X-ray images, employing an innovative approach: adapting the Xception model into a U-Net architecture via the Segmentation_Models package. Leveraging deep learning and image segmentation, the U-Net architecture, a CNN variant, proves ideal for this task, particularly after tailoring its output layer for classification. By utilizing the Xception model, we aim to enhance COVID-19 classification accuracy and efficiency. The results demonstrate promising autonomous identification of COVID-19 cases, offering valuable support to healthcare professionals. The fusion of medical imaging data with advanced neural network architectures highlights avenues for improving diagnostic accuracy during the pandemic. Notably, precision, recall, and F1 scores for each class are reported: Normal (Precision = 0.98, Recall = 0.9608, F1 Score = 0.9704), Pneumonia (Precision = 0.9579, Recall = 0.9579, F1 Score = 0.9579), and COVID-19 (Precision = 0.96, Recall = 0.9796, F1 Score = 0.9698). These findings underscore the effectiveness of our approach in accurately classifying COVID-19 cases from chest X-ray images, offering promising avenues for enhancing diagnostic capabilities during the pandemic.
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