Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT

Sun, Liang; Mo, Zhanhao; Yan, Fuhua; Xia, Liming; Shan, Fei; Ding, Zhongxiang; Song, Bin; Gao, Wanchun; Shao, Wei; Shi, Feng; Yuan, Huan; Jiang, Huiting; Wu, Dijia; Wei, Ying; Gao, Yaozong; Sui, He; Zhang, Daoqiang; Shen, Dinggang

doi:10.1109/jbhi.2020.3019505

Cited by 197 publications

(116 citation statements)

References 23 publications

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“…[38] Deep Learning Based CT 400 COVID-19 (＋) Cases, 350 COVID-19 (−) Cases Sun et al. [39] Adaptive Feature Selection guided Deep Forest CT 1495 Covid-19 (＋), 1027 community-acquired pneumonia (CAP) Jaiswal et al. [40] DenseNet201 based deep transfer learning CT 1262 COVID-19 (＋) Cases, 1230 COVID-19 (−) Cases Abraham et al.…”

Section: Related Workmentioning

confidence: 99%

A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods

Saygılı

2021

Applied Soft Computing

108

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The COVID-19 outbreak has been causing a global health crisis since December 2019. Due to this virus declared by the World Health Organization as a pandemic, the health authorities of the countries are constantly trying to reduce the spread rate of the virus by emphasizing the rules of masks, social distance, and hygiene. COVID-19 is highly contagious and spreads rapidly globally and early detection is of paramount importance. Any technological tool that can provide rapid detection of COVID-19 infection with high accuracy can be very useful to medical professionals. The disease findings on COVID-19 images, such as computed tomography (CT) and X-rays, are similar to other lung infections, making it difficult for medical professionals to distinguish COVID-19. Therefore, computer-aided diagnostic solutions are being developed to facilitate the identification of positive COVID-19 cases. The method currently used as a gold standard in detecting the virus is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Due to the high false-negative rate of this test and the delays in the test results, alternative solutions are sought. This study was conducted to investigate the contribution of machine learning and image processing to the rapid and accurate detection of COVID-19 from two of the most widely used different medical imaging modes, chest X-ray and CT images. The main purpose of this study is to support early diagnosis and treatment to end the coronavirus epidemic as soon as possible. One of the primary aims of the study is to provide support to medical professionals who are most worn out and working under intense stress during COVID-19 through smart learning methods and image classification models. The proposed approach was applied to three different public COVID-19 data sets and consists of five basic steps: data set acquisition, pre-processing, feature extraction, dimension reduction, and classification stages. Each stage has its sub-operations. The proposed model performs in considerable levels of COVID-19 detection for dataset-1 (CT), dataset-2 (X-ray) and dataset-3 (CT) with the accuracy of 89.41%, 99.02%, 98.11%, respectively. On the other hand, in the X-ray data set, an accuracy of 85.96% was obtained for COVID-19 (+), COVID-19 (-), and those with Pneumonia but not COVID-19 classes. As a result of the study, it has been shown that COVID-19 can be detected with a high success rate in about less than one minute with image processing and classical learning methods. In the light of the findings, it is possible to say that the proposed system will help radiologists in their decisions, will be useful in the early diagnosis of the virus, and can distinguish pneumonia caused by the COVID-19 virus from the pneumonia of other diseases.

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Section: Related Workmentioning

confidence: 99%

A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods

Saygılı

2021

Applied Soft Computing

108

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“…There were a total of 172 publications and 107 of them were original studies. Among these 107 original studies, 27 of them described using AI or ML for the analyses of radiological images [10][11][12] to improve the diagnostic accuracy of COVID-19. There was only one published study [13] using AI or ML to evaluate clinical and laboratory features for the classification of severity of COVID-19.…”

Section: Related Workmentioning

confidence: 99%

Machine learning discovery of distinguishing laboratory features for severity classification of COVID‐19 patients

Xiao

Zhang

et al. 2021

IET Cyber-Syst and Robotics

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386 confirmed cases have been reported worldwide, with an accumulative mortality of 233,093. Due to the complexity and uncertainty of the pathology of COVID-19, it is not easy for front-line doctors to categorise severity levels of clinical COVID-19 that are general and severe/critical cases, with consistency. The more than 300 laboratory features, coupled with underlying disease, all combine to complicate proper and rapid patient diagnosis. However, such screening is necessary for early triage, diagnosis, assignment of appropriate level of care facility, and institution of timely intervention. A machine learning analysis was carried out with confirmed COVID-19 patient data from 10 January to 18 February 2020, who were admitted to Tongji Hospital, in Wuhan, China. A softmax neural network-based machine learning model was established to categorise patient severity levels. According to the analysis of 2662 cases using clinical and laboratory data, the present model can be used to reveal the top 30 of more than 300 laboratory features, yielding 86.30% blind test accuracy, 0.8195 F1-score, and 100% consistency using a two-way patient classification of severe/critical to general. For severe/critical cases, F1-score is 0.9081 (i.e. recall is 0.9050, and precision is 0.9113). This model for classification can be accomplished at a mini-second-level computational cost (in contrast to minute-level manual). Based on available COVID-19 patient diagnosis and therapy, an artificial intelligence model paradigm can help doctors quickly classify patients with a high degree of accuracy and 100% consistency to significantly improve diagnostic and classification efficiency. The discovered top 30 laboratory features can be used for greater differentiation to serve as an essential supplement to current guidelines, thus creating a more

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“…The lungs of the infected cases have visual marks like ground-glass opacity or hazy darkened spots, which help to differentiate infected cases from normal controls (5). With good sensitivity (SEN) and speed, chest computed tomography (CT) has been widely used in automatic diagnosis methods (6)(7)(8)(9).…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network

et al. 2021

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A three-dimensional (3D) deep learning method is proposed, which enables the rapid diagnosis of coronavirus disease 2019 (COVID-19) and thus significantly reduces the burden on radiologists and physicians. Inspired by the fact that the current chest computed tomography (CT) datasets are diversified in equipment types, we propose a COVID-19 graph in a graph convolutional network (GCN) to incorporate multiple datasets that differentiate the COVID-19 infected cases from normal controls. Specifically, we first apply a 3D convolutional neural network (3D-CNN) to extract image features from the initial 3D-CT images. In this part, a transfer learning method is proposed to improve the performance, which uses the task of predicting equipment type to initialize the parameters of the 3D-CNN structure. Second, we design a COVID-19 graph in GCN based on the extracted features. The graph divides all samples into several clusters, and samples with the same equipment type compose a cluster. Then we establish edge connections between samples in the same cluster. To compute accurate edge weights, we propose to combine the correlation distance of the extracted features and the score differences of subjects from the 3D-CNN structure. Lastly, by inputting the COVID-19 graph into GCN, we obtain the final diagnosis results. In experiments, the dataset contains 399 COVID-19 infected cases, and 400 normal controls from six equipment types. Experimental results show that the accuracy, sensitivity, and specificity of our method reach 98.5%, 99.9%, and 97%, respectively.

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Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT

Cited by 197 publications

References 23 publications

A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods

A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods

Machine learning discovery of distinguishing laboratory features for severity classification of COVID‐19 patients

Diagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network

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