论文标题
冠状病毒（COVID-19）使用深度特征融合和排名技术进行分类
Coronavirus (COVID-19) Classification using Deep Features Fusion and Ranking Technique
论文作者
论文摘要
冠状病毒（COVID-19）于2019年底出现。世界卫生组织（WHO）被确定为全球流行病。达成共识的意见是，使用计算机断层扫描（CT）技术来早期诊断大流行疾病会带来快速，准确的结果。专家放射科医生指出，COVID-19在CT图像中显示不同的行为。在这项研究中，提出了一种新颖的方法为融合和排名深度特征，以在早期阶段检测Covid-19。从150个CT图像中获得16x16（子集-1）和32x32（子集）贴片以生成子数据集。在提出的方法的范围内，已将3000个贴片图像标记为Covid-19，在训练和测试阶段没有发现。已经应用了特征融合和排名方法，以提高所提出方法的性能。然后，将处理后的数据与支持向量机（SVM）分类。 According to other pre-trained Convolutional Neural Network (CNN) models used in transfer learning, the proposed method shows high performance on Subset-2 with 98.27% accuracy, 98.93% sensitivity, 97.60% specificity, 97.63% precision, 98.28% F1-score and 96.54% Matthews Correlation Coefficient (MCC) metrics.
Coronavirus (COVID-19) emerged towards the end of 2019. World Health Organization (WHO) was identified it as a global epidemic. Consensus occurred in the opinion that using Computerized Tomography (CT) techniques for early diagnosis of pandemic disease gives both fast and accurate results. It was stated by expert radiologists that COVID-19 displays different behaviours in CT images. In this study, a novel method was proposed as fusing and ranking deep features to detect COVID-19 in early phase. 16x16 (Subset-1) and 32x32 (Subset-2) patches were obtained from 150 CT images to generate sub-datasets. Within the scope of the proposed method, 3000 patch images have been labelled as CoVID-19 and No finding for using in training and testing phase. Feature fusion and ranking method have been applied in order to increase the performance of the proposed method. Then, the processed data was classified with a Support Vector Machine (SVM). According to other pre-trained Convolutional Neural Network (CNN) models used in transfer learning, the proposed method shows high performance on Subset-2 with 98.27% accuracy, 98.93% sensitivity, 97.60% specificity, 97.63% precision, 98.28% F1-score and 96.54% Matthews Correlation Coefficient (MCC) metrics.
