论文标题
使用卷积支持向量机对COVID-19中的Covid-19分类
Classification of COVID-19 in Chest CT Images using Convolutional Support Vector Machines
论文作者
论文摘要
目的：2019年冠状病毒（Covid-19）出现在中国武汉并影响了整个世界，已损失了成千上万人的生命。由于该病毒的迅速传播，手动诊断效率低下。因此，在人工智能算法的支持下进行了自动Covid-19检测研究。方法：在这项研究中，提出了检测高性能的COVID-19病例的深度学习模型。所提出的方法定义为卷积支持向量机（CSVM），并且可以自动对计算机断层扫描（CT）图像进行分类。与接受转移学习方法训练的预训练的卷积神经网络（CNN）不同，CSVM模型被训练为划痕。为了评估CSVM方法的性能，数据集分为两个部分，为培训（％75）和测试（％25）。 CSVM模型由包含三个不同数量的SVM内核的块组成。 Results: When the performance of pre-trained CNN networks and CSVM models is assessed, CSVM (7x7, 3x3, 1x1) model shows the highest performance with 94.03% ACC, 96.09% SEN, 92.01% SPE, 92.19% PRE, 94.10% F1-Score, 88.15% MCC and 88.07% Kappa metric values.结论：所提出的方法比其他方法更有效。在实验中证明，它是对抗共证和未来研究的灵感。
Purpose: Coronavirus 2019 (COVID-19), which emerged in Wuhan, China and affected the whole world, has cost the lives of thousands of people. Manual diagnosis is inefficient due to the rapid spread of this virus. For this reason, automatic COVID-19 detection studies are carried out with the support of artificial intelligence algorithms. Methods: In this study, a deep learning model that detects COVID-19 cases with high performance is presented. The proposed method is defined as Convolutional Support Vector Machine (CSVM) and can automatically classify Computed Tomography (CT) images. Unlike the pre-trained Convolutional Neural Networks (CNN) trained with the transfer learning method, the CSVM model is trained as a scratch. To evaluate the performance of the CSVM method, the dataset is divided into two parts as training (%75) and testing (%25). The CSVM model consists of blocks containing three different numbers of SVM kernels. Results: When the performance of pre-trained CNN networks and CSVM models is assessed, CSVM (7x7, 3x3, 1x1) model shows the highest performance with 94.03% ACC, 96.09% SEN, 92.01% SPE, 92.19% PRE, 94.10% F1-Score, 88.15% MCC and 88.07% Kappa metric values. Conclusion: The proposed method is more effective than other methods. It has proven in experiments performed to be an inspiration for combating COVID and for future studies.
