ctcovid -19:使用深度学习的计算机断层扫描自动Covid-19模型

Carlos Antunes , João Rodrigues , António Cunha
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引用次数: 0

摘要

COVID-19是由严重急性呼吸系统综合征冠状病毒2 (SARS-CoV-2)引发的一种极具传染性的呼吸道疾病。常见症状包括发烧、咳嗽、疲劳和呼吸困难,严重时往往导致住院治疗和死亡。ctcovid -19是为COVID-19检测量身定制的新模型,专门针对独特的深度学习结构,使用ImageNet训练的ResNet-50是我们模型的基础框架。为了增强其在计算机断层扫描中捕捉与COVID-19模式相关的相关特征的能力,该网络通过图层调整和添加新图层进行了微调。该模型在三个广泛认可和记录的专用于COVID-19检测的数据集中实现了97.0%至99.8%的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CTCovid19: Automatic Covid-19 model for Computed Tomography Scans Using Deep Learning
COVID-19 is an extremely contagious respiratory sickness instigated by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Common symptoms encompass fever, cough, fatigue, and breathing difficulties, often leading to hospitalization and fatalities in severe cases. CTCovid19 is a novel model tailored for COVID-19 detection, specifically honing in on a distinct deep learning structure, ResNet-50 trained with ImageNet serves as the foundational framework for our model. To enhance its capability to capture pertinent features related to COVID-19 patterns in Computed Tomography scans, the network underwent fine-tuning through layer adjustments and the addition of new ones. The model achieved accuracy rates that went from 97.0 % to 99.8 % across three widely recognized and documented datasets dedicated to COVID-19 detection.
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来源期刊
Intelligence-based medicine
Intelligence-based medicine Health Informatics
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5.00
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187 days
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