论文标题
使用转移学习从胸部CT扫描中对其严重程度的COVID-19患者分类
Classification of COVID-19 Patients with their Severity Level from Chest CT Scans using Transfer Learning
论文作者
论文摘要
背景和目标：在大流行期间，使用人工智能（AI）方法与生物医学科学相结合，在减轻医疗保健系统和医生的负担方面起着重要作用。 Covid-19的速度迅速增加导致对医院病床和其他医疗设备的需求增加。但是，由于医疗设施受到限制，建议根据感染的严重程度诊断患者。牢记这一点，我们分享了我们在检测Covid-19的研究以及使用胸部CT扫描和深度学习预训练的模型来评估其严重性方面的研究。数据集：我们总共为三个不同的类标签收集了1966年的CT扫描图像，即，非兴奋，严重的库维德和非统治covid，其中714个CT图像属于非杂化类别，713个CT图像对于非severe covid类别和539个CT图像是严重的Covid类别。方法：所有图像最初都是使用对比度有限的直方图均衡（CLAHE）方法进行预处理的。然后将预处理的图像送入VGG-16网络中以提取功能。最后，将检索到的特性分类，并使用具有10倍交叉验证（CV）的支持向量机（SVM）评估精度。结果和结论：在我们的研究中，我们结合了众所周知的预处理，提取和分类的策略，使我们获得了显着的疾病成功率及其严重性识别率，精度为96.05％（非塞维利共vid-19的非severe covid-9图像为97.7％，严重的covid-19％的图像为93％）。因此，我们的模型可以帮助放射科医生检测Covid-19及其严重程度的程度。
Background and Objective: During pandemics, the use of artificial intelligence (AI) approaches combined with biomedical science play a significant role in reducing the burden on the healthcare systems and physicians. The rapid increment in cases of COVID-19 has led to an increase in demand for hospital beds and other medical equipment. However, since medical facilities are limited, it is recommended to diagnose patients as per the severity of the infection. Keeping this in mind, we share our research in detecting COVID-19 as well as assessing its severity using chest-CT scans and Deep Learning pre-trained models. Dataset: We have collected a total of 1966 CT Scan images for three different class labels, namely, Non-COVID, Severe COVID, and Non-Severe COVID, out of which 714 CT images belong to the Non-COVID category, 713 CT images are for Non-Severe COVID category and 539 CT images are of Severe COVID category. Methods: All of the images are initially pre-processed using the Contrast Limited Histogram Equalization (CLAHE) approach. The pre-processed images are then fed into the VGG-16 network for extracting features. Finally, the retrieved characteristics are categorized and the accuracy is evaluated using a support vector machine (SVM) with 10-fold cross-validation (CV). Result and Conclusion: In our study, we have combined well-known strategies for pre-processing, feature extraction, and classification which brings us to a remarkable success rate of disease and its severity recognition with an accuracy of 96.05% (97.7% for Non-Severe COVID-19 images and 93% for Severe COVID-19 images). Our model can therefore help radiologists detect COVID-19 and the extent of its severity.
