A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19)

Farid, Ahmed Abdullah; Selim, Gamal M.; Khater, Hatem A.

doi:10.14299/ijser.2020.03.02

Cited by 54 publications

(45 citation statements)

References 28 publications

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“…13, No. 5,2020 DOI: 10.22266/ijies2020.1031.07 since opacification and consolidation were present early [3]. Artificial Intelligence (AI) techniques can be used with X-Ray image of the chest region to detect and follow-up of the disease.…”

Section: Introductionmentioning

confidence: 99%

“…AI algorithms and discriminative features derived from the X-Ray image would be of big support to undertake Computer Aided Diagnostic (CAD) program that could be used in any country with access to X-Ray equipment to help in the diagnosis of COVID-19 [4]. Farid et al (2020) presented a technique for recognizing the COVID-19 in CT images by proposing a Composite Hybrid Feature Extraction (CHFS). The selected features were classified by the Stack Hybrid Classification system (SHC).…”

Section: Introductionmentioning

confidence: 99%

“…A total of 51 CT-images collected from Kaggle database website were used for model evaluation. An accuracy of about 96.07% has been achieved when using a Naïve Bayes as a meta-classifier in a hybrid classification [5]. Xu et al (2020) established an early screening model using deep learning techniques to distinguish COVID-19 pneumonia from just Influenza pneumonia using CT images.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Computer Aided Diagnostic for COVID-19 Based on Chest X-Ray Image and Particle Swarm Intelligence

Mohammed¹,

Alkinani²,

Hassan³

2020

IJIES

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COVID-19 is a vital zoonotic illness caused by Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2). COVID-19 is a very widespread among humans thus the early detection and curing of the disease offers a high opportunity of survival for patients. Computed Tomography (CT) plays an important role in the diagnosis of COVID-19. As chest radiography can give an indicator of coronavirus. Though, an automated Computer Aided Diagnostic (CAD) system for COVID-19 based on chest X-Ray image analysis is presented in this article. It is designed for COVID-19 recognition from other MERS, SARS, and ARDS viral pneumonia. The optimal threshold value for the segmentation of a chest image is deduced by exploiting Li s' method and particle swarm intelligence. Laws' masks are then applied to the segmented chest image for secondary characteristics highlighting. After that, nine different vectors of attributes are extracted from the Grey Level Co-occurrence Matrix (GLCM) representation of each Law's mask result. Support vector machine ensemble models are then built based on the extracted feature vectors. Finally, a weighted voting method is utilized to combine the decisions of ensemble classifiers. Experimental findings show an accuracy of 98.04 %. It indicates that the suggested CAD scheme can be a promising supplementary COVID-19 diagnostic tool for clinical doctors.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Computer Aided Diagnostic for COVID-19 Based on Chest X-Ray Image and Particle Swarm Intelligence

Mohammed¹,

Alkinani²,

Hassan³

2020

IJIES

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“…[ 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 115 ]…”

Section: Uncited Referencesunclassified

A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic

Rasheed

Jamıl

Hameed

et al. 2020

Chaos, Solitons & Fractals

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Highlights The current crisis related to the spread of COVID-19 has challenged epidemiologists and public health experts alike, leading to a rapid search for, and development of, new and innovative solutions to combat its spread. A multidisciplinary approach needs to be followed for diagnosis, treatment and tracking, especially between medical and computer sciences, so, a common ground is available to facilitate the research work at a faster pace. This review paper covers both medical and technological perspectives to facilitate the virologists, AI researchers and policymakers while in combating the COVID-19 outbreak. This paper aimed to explore and understand how and which different technological tools and techniques have been used within the context of COVID-19. Investigating Artificial Intelligence (AI) approaches for the diagnosis, anticipate infection and mortality rate by tracing contacts and targeted drug designing. The impact of different kinds of medical data used in diagnosis, prognosis and pandemic analysis is also provided. The investigation of this paper reveals several AI-based approaches that have been proposed as potential ways to help, with the COVID-19 pandemic, covering everything from initial diagnoses via image diagnostics up to the presentation of models that help to understand the spread of COVID-19 and identify potential new outbreak areas.

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“…We have summarized them in Table 1. For example, in most method of [45] and in [1,32,[36][37][38]40,42], authors used non-deep learning methods, such as k-NN, LR, Cox, SVM and DT to classify CT/X-ray images and predict the outcomes of COVID-19 patients.…”

Section: Introductionmentioning

confidence: 99%

Predicting COVID-19 Disease Progression and Patient Outcomes based on Temporal Deep Learning

Sun

Hong

Song

et al. 2020

Preprint

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Background: The coronavirus disease 2019 (COVID-19) pandemic has caused health concerns worldwide since December 2019. From the beginning of infection, patients will progress through different symptom stages, such as fever, dyspnea or even death. Identifying disease progression and predicting patient outcome at an early stage helps target treatment and resource allocation. However, there is no clear COVID-19 stage definition, and few studies have addressed characterizing COVID-19 progression, making the need for this study evident.Methods: We proposed a temporal deep learning method, based on a time-aware long short-term memory (T-LSTM) neural network and used an online open dataset, including blood samples of 485 patients from Wuhan, China, to train the model. Our method can grasp the dynamic relations in irregularly sampled time series, which is ignored by existing works. Specifically, our method predicted the outcome of COVID-19 patients by considering both the biomarkers and the irregular time intervals. Then, we used the patient representations, extracted from T-LSTM units, to subtype the patient stages and describe the disease progression of COVID-19.Results: Using our method, the accuracy of the outcome of prediction results was more than 90% at 12 days and 98%, 95% and 93% at 3, 6, and 9 days, respectively. Most importantly, we found 4 stages of COVID-19 progression with different patient statuses and mortality risks. We ranked 40 biomarkers related to disease and gave the reference values of them for each stage. Top 5 is Lymph, LDH, hs-CRP, Indirect Bilirubin, Creatinine. Besides, we have found 3 complications - myocardial injury, liver function injury and renal function injury. Predicting which of the 4 stages the patient is currently in can help doctors better assess and cure the patient. Conclusions: To combat the COVID-19 epidemic, this paper aims to help clinicians better assess and treat infected patients, provide relevant researchers with potential disease progression patterns, and enable more effective use of medical resources. Our method predicted patient outcomes with high accuracy and identified a four-stage disease progression. We hope that the obtained results and patterns will aid in fighting the disease.

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A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19)

Cited by 54 publications

References 28 publications

Automatic Computer Aided Diagnostic for COVID-19 Based on Chest X-Ray Image and Particle Swarm Intelligence

Automatic Computer Aided Diagnostic for COVID-19 Based on Chest X-Ray Image and Particle Swarm Intelligence

A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic

Predicting COVID-19 Disease Progression and Patient Outcomes based on Temporal Deep Learning

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