Prediction of COVID-19 - Pneumonia based on Selected Deep Features and One Class Kernel Extreme Learning Machine

Khan, Muhammad Attique; Kadry, Seifedine; Zhang, Yudong; Akram, Tallha; Sharif, Muhammad; Rehman, Amjad

doi:10.1016/j.compeleceng.2020.106960

Cited by 121 publications

(72 citation statements)

References 22 publications

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“…One such method is extreme learning machine (ELM) [18], which is a kind of artificial neural network (ANN). ELM has been applied to various applications such as colorectal cancer [19]; thyroid disease [20]; Parkinson's disease [21]; brain tumors [22]; osteoarthritis [23]; and most recently, COVID-19 pneumonia [24] diagnosis. Due to the advantages of fast learning speed and low computational cost, ELMs have become popular for solving many complex problems.…”

Section: Introductionmentioning

confidence: 99%

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

et al. 2021

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Globally, breast cancer is one of the most significant causes of death among women. Early detection accompanied by prompt treatment can reduce the risk of death due to breast cancer. Currently, machine learning in cloud computing plays a pivotal role in disease diagnosis, but predominantly among the people living in remote areas where medical facilities are scarce. Diagnosis systems based on machine learning act as secondary readers and assist radiologists in the proper diagnosis of diseases, whereas cloud-based systems can support telehealth services and remote diagnostics. Techniques based on artificial neural networks (ANN) have attracted many researchers to explore their capability for disease diagnosis. Extreme learning machine (ELM) is one of the variants of ANN that has a huge potential for solving various classification problems. The framework proposed in this paper amalgamates three research domains: Firstly, ELM is applied for the diagnosis of breast cancer. Secondly, to eliminate insignificant features, the gain ratio feature selection method is employed. Lastly, a cloud computing-based system for remote diagnosis of breast cancer using ELM is proposed. The performance of the cloud-based ELM is compared with some state-of-the-art technologies for disease diagnosis. The results achieved on the Wisconsin Diagnostic Breast Cancer (WBCD) dataset indicate that the cloud-based ELM technique outperforms other results. The best performance results of ELM were found for both the standalone and cloud environments, which were compared. The important findings of the experimental results indicate that the accuracy achieved is 0.9868, the recall is 0.9130, the precision is 0.9054, and the F1-score is 0.8129.

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Section: Introductionmentioning

confidence: 99%

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

et al. 2021

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“…Infectious disease transmission is a complex transmission process that takes place from human to human. In addition to different symptoms of this infection, it has highly destructive effects on the lungs, causing a break in the respirate system that may lead to death (Khan et al, 2021). Indeed, every country tried to handle this pandemic through different strategies such as partial or complete lockdown to stop the spread‐out of this virus or herd immunity from creating sufficient resistance among the people to cover this infection through the body immune system.…”

Section: Introductionmentioning

confidence: 99%

Machine learning techniques to detect and forecast the daily total COVID‐19 infected and deaths cases under different lockdown types

Saba

Abunadi

Shahzad

et al. 2021

Microscopy Res & Technique

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COVID‐19 has impacted the world in many ways, including loss of lives, economic downturn and social isolation. COVID‐19 was emerged due to the SARS‐CoV‐2 that is highly infectious pandemic. Every country tried to control the COVID‐19 spread by imposing different types of lockdowns. Therefore, there is an urgent need to forecast the daily confirmed infected cases and deaths in different types of lockdown to select the most appropriate lockdown strategies to control the intensity of this pandemic and reduce the burden in hospitals. Currently are imposed three types of lockdown (partial, herd, complete) in different countries. In this study, three countries from every type of lockdown were studied by applying time‐series and machine learning models, named as random forests, K‐nearest neighbors, SVM, decision trees (DTs), polynomial regression, Holt winter, ARIMA, and SARIMA to forecast daily confirm infected cases and deaths due to COVID‐19. The models' accuracy and effectiveness were evaluated by error based on three performance criteria. Actually, a single forecasting model could not capture all data sets' trends due to the varying nature of data sets and lockdown types. Three top‐ranked models were used to predict the confirmed infected cases and deaths, the outperformed models were also adopted for the out‐of‐sample prediction and obtained very close results to the actual values of cumulative infected cases and deaths due to COVID‐19. This study has proposed the auspicious models for forecasting and the best lockdown strategy to mitigate the causalities of COVID‐19.

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“…In the proposed model, an extreme learning machine classifier was used, together with a 15-layer CNN that extracts deep features. The average accuracy, sensitivity, specificity and precision values were 95.1%, 95.1%, 95% and 94%, respectively [ 26 ]. Amyar et al proposed a multi-task deep learning architecture to both detect and classify COVID-19 lesions in CT scans.…”

Section: Related Workmentioning

confidence: 99%

Scat-NET: COVID-19 diagnosis with a CNN model using scattergram images

Tuncer

Ayyıldız

Kalaycı

et al. 2021

Computers in Biology and Medicine

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The acute respiratory syndrome COVID-19 disease, which is caused by SARS-CoV-2, has infected many people over a short time and caused the death of more than 2 million people. The gold standard in detecting COVID-19 is to apply the reverse transcription polymerase chain reaction (RT-PCR) test. This test has low sensitivity and produces false results of approximately 15%–20%. Computer tomography (CT) images were checked as a result of suspicious RT-PCR tests. If the virus is not infected in the lung, the virus is not observed on CT lung images. To overcome this problem, we propose a 25-depth convolutional neural network (CNN) model that uses scattergram images, which we call Scat-NET. Scattergram images are frequently used to reveal the numbers of neutrophils, eosinophils, basophils, lymphocytes and monocytes, which are measurements used in evaluating disease symptoms, and the relationships between them. To the best of our knowledge, using the CNN together with scattergram images in the detection of COVID-19 is the first study on this subject. Scattergram images obtained from 335 patients in total were classified using the Scat-NET architecture. The overall accuracy was 92.4%. The most striking finding in the results obtained was that COVID-19 patients with negative RT-PCR tests but positive CT test results were positive. As a result, we emphasize that the Scat-NET model will be an alternative to CT scans and could be applied as a secondary test for patients with negative RT-PCR tests.

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Prediction of COVID-19 - Pneumonia based on Selected Deep Features and One Class Kernel Extreme Learning Machine

Cited by 121 publications

References 22 publications

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

Machine learning techniques to detect and forecast the daily total COVID‐19 infected and deaths cases under different lockdown types

Scat-NET: COVID-19 diagnosis with a CNN model using scattergram images

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