COVID-19 Detection Empowered with Machine Learning and Deep Learning Techniques: A Systematic Review

Rehman, Amir; Iqbal, Muhammad Azhar; Xing, Huanlai; Ahmed, Irfan

doi:10.3390/app11083414

Cited by 54 publications

(45 citation statements)

References 74 publications

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“…Remarkable progress has been made in the automated detection of COVID-19 in CXRs [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Several studies in the literature have leveraged deep convolutional neural networks (CNNs) with and without modifications to convincingly predict COVID-19.…”

Section: Related Workmentioning

confidence: 99%

“…To remedy this issue, researchers have applied multi-classification of COVID-19 from normal, bacterial pneumonia, TB. Multi-classification methods have improved the detection accuracy of COVID-19 [9].…”

Section: Related Workmentioning

confidence: 99%

“…The weight of each class could be computed using Equations ( 7) and ( 8). The weighted cross-entropy for each training sample is defined as Equation (9). Figure 8b plots the equally contributed frequency of each class after loss-weighting.…”

Section: Approach_1: Deep Learning With Weighted Loss On An Unbalanced Datasetmentioning

confidence: 99%

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Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset

et al. 2021

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The ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages incurred by the virus. This study aimed to leverage deep-learning-based methods toward the automated classification of COVID-19 from normal and viral pneumonia on CXRs, and the identification of indicative regions of COVID-19 biomarkers. Initially, we preprocessed and segmented the lung regions usingDeepLabV3+ method, and subsequently cropped the lung regions. The cropped lung regions were used as inputs to several deep convolutional neural networks (CNNs) for the prediction of COVID-19. The dataset was highly unbalanced; the vast majority were normal images, with a small number of COVID-19 and pneumonia images. To remedy the unbalanced distribution and to avoid biased classification results, we applied five different approaches: (i) balancing the class using weighted loss; (ii) image augmentation to add more images to minority cases; (iii) the undersampling of majority classes; (iv) the oversampling of minority classes; and (v) a hybrid resampling approach of oversampling and undersampling. The best-performing methods from each approach were combined as the ensemble classifier using two voting strategies. Finally, we used the saliency map of CNNs to identify the indicative regions of COVID-19 biomarkers which are deemed useful for interpretability. The algorithms were evaluated using the largest publicly available COVID-19 dataset. An ensemble of the top five CNNs with image augmentation achieved the highest accuracy of 99.23% and area under curve (AUC) of 99.97%, surpassing the results of previous studies.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset

et al. 2021

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“…In order to overcome these existing shortcomings, many computer-aided diagnostic tools (CADTs) using artificial intelligence (AI) and machine learning techniques have been utilized to support clinical findings from the imaging modalities [ 20 ]. These prediction techniques can precisely detect the disease, thereby aiding in the prevention and detection of epidemiologic risk [ 21 ]. These automated tools using cost-effective imaging modalities assist to address COVID-19 by preventing false negative reports, and can be used in the case of scarcity or non-availability of RT-PCR test kits in rural areas.…”

Section: Introductionmentioning

confidence: 99%

Role of Artificial Intelligence in COVID-19 Detection

Gudigar

Raghavendra

Nayak

et al. 2021

Sensors

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The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.

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“…The 2019 coronavirus disease (COVID- 19) is a very contagious viral infection disease and thus far remains to be spread aggressively and has become a serious global health crisis [1][2][3]. So far, this disease has affected almost all countries with more than 2.5 million deaths worldwide [4]. The clinical manifestations of virus ranged from asymptomatic or mild flu like symptoms to severe complications including respiratory insufficiency and intensive care unit (ICU) hospitalization, where patients may be intubated for mechanical ventilation and ultimately death [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Afrash

Kazemi-Arpanahi²,

Ranjbar

et al. 2021

J. Med. Chem. Sci.

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The rapid worldwide outbreak of COVID-19 has posed serious and unprecedented challenges to healthcare systems in predicting disease behavior, consequences and resource utilization. Therefore, predicting the Length of Stay (LOS) is necessary to ensure optimal allocate of scarce hospital resources. The purpose of this research was to construct a model for predicting COVID-19 patients' hospital LOS by multiple Machine Learning (ML) algorithms. Using a single-center registry, we studied the records of 1225 laboratory-confirmed COVID-19 hospitalized patients from February 9, 2020, to December 20, 2020. The most important clinical parameters in the COVID-19 LOS prediction were identified with a correlation coefficient at the P-value< 0.2. Then, the prediction models were developed based on seven ML techniques according to selected variables. Finally, to evaluate the performances of those models several standard quantitative measures includes accuracy, sensitivity, specificity and ROC curve were used to evaluate the proposed predictive models. After implementing feature selection, a total of 20 variables was identified as the most relevant predictors to build the prediction models. The results indicated that the best performance belonged to the Support Vector Machine (SVM) algorithm with the mean accuracy of 99.5%, mean specificity of 99.7%, mean sensitivity of 99.4%, and the standard deviation of 1.2. The SVM provided a reasonable level of accuracy and certainty in predicting the LOS in COVID-19 patients and potentially facilitates hospital bed management, turnover and optimized resource allocation.

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COVID-19 Detection Empowered with Machine Learning and Deep Learning Techniques: A Systematic Review

Cited by 54 publications

References 74 publications

Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset

Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset

Role of Artificial Intelligence in COVID-19 Detection

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