Classification of COVID-19 by Compressed Chest CT Image through Deep Learning on a Large Patients Cohort

Abstract

Corona Virus Disease (COVID-19) has spread globally quickly, and has resulted in a large number of causalities and medical resources insufficiency in many countries. Reverse-transcriptase polymerase chain reaction (RT-PCR) testing is adopted as biopsy tool for confirmation of virus infection. However, its accuracy is as low as 60-70%, which is inefficient to uncover the infected. In comparison, the chest CT has been considered as the prior choice in diagnosis and monitoring progress of COVID-19 infection. Although the COVID-19 diagnostic systems based on artificial intelligence have been developed for assisting doctors in diagnosis, the small sample size and the excessive time consumption limit their applications. To this end, this paper proposed a diagnosis prototype system for COVID-19 infection testing. The proposed deep learning model is trained and is tested on 2267 CT sequences from 1357 patients clinically confirmed with COVID-19 and 1235 CT sequences from non-infected people. The main highlights of the prototype system are: (1) no data augmentation is needed to accurately discriminate the COVID-19 from normal controls with the specificity of 0.92 and sensitivity of 0.93; (2) the raw DICOM image is not necessary in testing. Highly compressed image like Jpeg can be used to allow a quick diagnosis; and (3) it discriminates the virus infection within 6 seconds and thus allows an online test with light cost. We also applied our model on 48 asymptomatic patients diagnosed with COVID-19. We found that: (1) the positive rate of RT-PCR assay is 63.5% (687/1082). (2) 45.8% (22/48) of the RT-PCR assay is negative for asymptomatic patients, yet the accuracy of CT scans is 95.8%. The online detection system is available: http://212.64.70.65/covid. Ziwei Zhu, Guihua Tao and Tingting Dan have contributed equally to this study and are considered as joint first authors.

Deep Learning–Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images

“…The results of the experiments for COVID-19/non-COVID-19 and COVID-19 pneumonia/other pneumonia classifications are shared in Tables 6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,and 31. In this section, the results are evaluated.…”

Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks

“…The results of both systems were further fused and compared with the results of the separate systems; the joint decision-making results were better than those of the sub-systems. Zhu et al [ 71 ] recycled the pre-trained ResNet-50, a feature extractor, and a classifier. The model was fine-tuned on the COVID-19 dataset using pre-trained weights from the ImageNet dataset to avoid overfitting, ultimately differentiating between COVID-19 and non-COVID-19 pneumonia.…”

Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights

“…In another research work [59], a diagnosis prototype system based on ResNet50 architecture was proposed. The used COVID-19 CT dataset of the study was obtained from Huangpi Hospital of Traditional Chinese Medicine, Wuhan, China.…”

“…Both branch outputs are then combined and used to perform a classification. The data were collected from two open-source datasets of chest X-ray and CT images [53][54][55][56][57][58][59][60][61][62]. The first dataset consisted of 351 chest X-ray and CT images, which were positive or suspected of COVID-19.…”