Automatic classification of severity of <scp>COVID</scp>‐19 patients using texture feature and random forest based on computed tomography images

Amini, Nasrin; Shalbaf, Ahmad

doi:10.1002/ima.22679

Cited by 28 publications

(18 citation statements)

References 37 publications

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“…Amini and Shalbaf [ 3 ] uses CT scans to classify the severity of the COVID-19 into four stages normal, mild, moderate and severe. 28 statistical texture features are extracted which are skewness, kurtosis and variance GLCM with 23 features, GLRLM and GLSZM.…”

Section: Related Workmentioning

confidence: 99%

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COVID-19 CT-images diagnosis and severity assessment using machine learning algorithm

2023

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As a pandemic, the primary evaluation tool for coronavirus (COVID-19) still has serious flaws. To improve the existing situation, all facilities and tools available in this field should be used to combat the pandemic. Reverse transcription polymerase chain reaction is used to evaluate whether or not a person has this virus, but it cannot establish the severity of the illness. In this paper, we propose a simple, reliable, and automatic system to diagnose the severity of COVID-19 from the CT scans into three stages: mild, moderate, and severe, based on the simple segmentation method and three types of features extracted from the CT images, which are ratio of infection, statistical texture features (mean, standard deviation, skewness, and kurtosis), GLCM and GLRLM texture features. Four machine learning techniques (decision trees (DT), K-nearest neighbors (KNN), support vector machines (SVM), and Naïve Bayes) are used to classify scans. 1801 scans are divided into four stages based on the CT findings in the scans and the description file found with the datasets. Our proposed model divides into four steps: preprocessing, feature extraction, classification, and performance evaluation. Four machine learning algorithms are used in the classification step: SVM, KNN, DT, and Naive Bayes. By SVM method, the proposed model achieves 99.12%, 98.24%, 98.73%, and 99.9% accuracy for COVID-19 infection segmentation at the normal, mild, moderate, and severe stages, respectively. The area under the curve of the model is 0.99. Finally, our proposed model achieves better performance than state-of-art models. This will help the doctors know the stage of the infection and thus shorten the time and give the appropriate dose of treatment for this stage.

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Section: Related Workmentioning

confidence: 99%

“…In our work, we use 16 features to detect the stage of infection. These features are the ratio of white regions of lesions to white regions of the lung (Ratio of Infection) [ 22 ], global statistical texture features, GLCM and GLRLM texture features [ 3 , 17 , 25 ].…”

Section: Our Proposed Approachmentioning

confidence: 99%

COVID-19 CT-images diagnosis and severity assessment using machine learning algorithm

2023

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“…The model had an overall accuracy of 96.18%, a precision of 95.46%, a sensitivity of 96.98% and an F-1 score of 96.21% with SVM. Research in [ 108 ], classifies the severity of COVID-19 positive CT images through feature extraction. The text features are then classified using Random Forest.…”

Section: Covid-19 Prediction Using Deep Learningmentioning

confidence: 99%

A Comprehensive Review of Machine Learning Used to Combat COVID-19

et al. 2022

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Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.

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“…In one study, texture features and a random forest classifier are used for the classification of severity of COVID-19 patients. 26 In another study, the changes in chest radiographs CT of 21 COVID-19 patients were determined on the basis of initial diagnosis until the patient recovered. 27 From that study, most patients who recovered from COVID-19, had the most lung changes on CT scans approximately 10 days after symptom onset.…”

Section: Introductionmentioning

confidence: 99%

Automatic diagnosis of severity of COVID-19 patients using an ensemble of transfer learning models with convolutional neural networks in CT images

Shalbaf

Gifani

Mehri-Kakavand

et al. 2022

Polish Journal of Medical Physics and Engineering

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Introduction: Quantification of lung involvement in COVID-19 using chest Computed tomography (CT) scan can help physicians to evaluate the progression of the disease or treatment response. This paper presents an automatic deep transfer learning ensemble based on pre-trained convolutional neural networks (CNNs) to determine the severity of COVID -19 as normal, mild, moderate, and severe based on the images of the lungs CT. Material and methods: In this study, two different deep transfer learning strategies were used. In the first procedure, features were extracted from fifteen pre-trained CNNs architectures and then fed into a support vector machine (SVM) classifier. In the second procedure, the pre-trained CNNs were fine-tuned using the chest CT images, and then features were extracted for the purpose of classification by the softmax layer. Finally, an ensemble method was developed based on majority voting of the deep learning outputs to increase the performance of the recognition on each of the two strategies. A dataset of CT scans was collected and then labeled as normal (314), mild (262), moderate (72), and severe (35) for COVID-19 by the consensus of two highly qualified radiologists. Results: The ensemble of five deep transfer learning outputs named EfficientNetB3, EfficientNetB4, InceptionV3, NasNetMobile, and ResNext50 in the second strategy has better results than the first strategy and also the individual deep transfer learning models in diagnosing the severity of COVID-19 with 85% accuracy. Conclusions: Our proposed study is well suited for quantifying lung involvement of COVID-19 and can help physicians to monitor the progression of the disease.

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Automatic classification of severity of COVID‐19 patients using texture feature and random forest based on computed tomography images

Cited by 28 publications

References 37 publications

COVID-19 CT-images diagnosis and severity assessment using machine learning algorithm

COVID-19 CT-images diagnosis and severity assessment using machine learning algorithm

A Comprehensive Review of Machine Learning Used to Combat COVID-19

Automatic diagnosis of severity of COVID-19 patients using an ensemble of transfer learning models with convolutional neural networks in CT images

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