通过深度学习方法早期诊断肺部感染

Marwa A. Shames, Mohammed Y. Kamil
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引用次数: 0

摘要

据医院称,COVID-19 在全球迅速传播,而 RT-PCR 检测在早期感染阶段并不敏感。要找到Covid-19,需要一种快速、准确的检测方法。CT 扫描显示了诊断的准确性。使用深度学习架构进行CT扫描处理可改善疾病诊断和治疗。利用CT扫描特征衍生出一种用于COVID-19检测的深度学习系统。通过使用和比较多个迁移学习模型、微调和嵌入过程,获得了最佳的感染诊断结果。使用 2482 张 CT 扫描图像对所有模型的诊断效果进行了评估。优化后的模型显著提高了灵敏度指标(86.26±1.72),这是准确检测 COVID-19 感染的关键因素,结果令人鼓舞。此外,由此产生的模型在准确性(81.15±0.17)、特异性(77.90±1.33)、精确性(76.79±0.80)、F1_score(81.24±0.37)和 AUC(81.88±0.2)等方面都表现出了较高的数值。深度学习方法已被有效地用于检测胸部 CT 扫描图像中的 COVID-19。未来,临床从业人员可能会采用所建议的方法来研究、识别和有效缓解更多的流行病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Early Diagnosis of Lung Infection via Deep Learning Approach
The rapid global spread of COVID-19 and RT-PCR tests are insensitive in early infection phases, according to hospitals. To find Covid-19, a fast, accurate test is needed. CT scans have shown diagnostic accuracy. CT scan processing using a deep learning architecture may improve illness diagnosis and treatment. A deep learning system for COVID-19 detection was derived using CT scan features. Using and comparing numerous transfer-learning models, fine-tuning, and the embedding process yielded the best infection diagnostic results. All models' diagnostic effectiveness was assessed using 2482 CT scan images. The optimized model demonstrated encouraging outcomes by significantly enhancing the sensitivity metric (86.26±1.72), a critical factor in accurately detecting COVID-19 infection. Additionally, the resulting model demonstrated elevated values for accuracy (81.15±0.17), specificity (77.90±1.33), precision (76.79±0.80), F1_score (81.24±0.37), and AUC (81.88±0.2). Deep learning methodologies have been effectively employed to detect COVID-19 in chest CT scan images. In the future, the suggested approach may be employed by clinical practitioners to study, identify, and effectively mitigate a greater number of pandemics.
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