A novel framework for rapid diagnosis of COVID-19 on computed tomography scans

Akram, Tallha; Attique, Muhammad; Gul, Salma; Shahzad, Aamir; Altaf, Muhammad; Naqvi, Salma; Damaševičius, Robertas; Maskeliūnas, Rytis

doi:10.1007/s10044-020-00950-0

Cited by 75 publications

(59 citation statements)

References 37 publications

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“…Using the proposed framework, the achieved accuracy using the Naive Bayes classifier is 92.6%, 92.6%, whereas other classifiers (EBT, L-SVM and F-KNN) behave significantly better to achieve an average accuracy of 92.2%, 92.1%, 92.2%, 92.1% and 92.0%, 92.0%, respectively. From the sensitivity and specificity values, the proposed framework was successfully managed to achieve high true positive and negative rates [88].…”

Section: Ai In the Identification Of Covid-19 Pneumonia And Its Complications At Chest Ctmentioning

confidence: 99%

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

et al. 2021

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Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.

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Section: Ai In the Identification Of Covid-19 Pneumonia And Its Complications At Chest Ctmentioning

confidence: 99%

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

et al. 2021

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“…In general, several approaches for diagnosing COVID‐19 are available, such as nucleic acid‐based methods using polymerase chain reaction (PCR) (Abdulkareem, Mohammed, et al, 2021 ; Esbin et al, 2020 ), next‐generation sequencing (Harris et al, 2013 ), computed tomography (CT) scan (Akram et al, 2021 ), chest X‐ray (CXR) (Mohammed et al, 2020 ; Pan, Guan, et al, 2020 ; Sahlol et al, 2020 ; Shi et al, 2020 ) and paper‐based detection (K. Mao et al, 2020 ). These methods are used in monitoring changes in organs, and patients may need to undergo these pathological tests.…”

Section: Introductionmentioning

confidence: 99%

Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches

et al. 2021

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COVID‐19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Recently, COVID‐19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID‐19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID‐19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID‐19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID‐19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID‐19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID‐19 and inspire their works for future development.

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“…Indeed, early works on COVID-19 imagery identified the existence of pulmonary lesions in non-severe and even in recovered patients [11]. In this manner, Akram et al [12] preprocessed CT data by proposing extraction and selection schemes of relevant features to classify COVID-19 and normal scans. They tested several classifiers obtaining an accuracy of 92.6% with a Naive Bayes classifier.…”

Section: Introductionmentioning

confidence: 99%

Deep-learning based detection of COVID-19 using lung ultrasound imagery

Diaz-Escobar¹,

et al. 2021

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Background The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, especially in underdeveloped countries. There is a clear need to develop novel computer-assisted diagnosis tools to provide rapid and cost-effective screening in places where massive traditional testing is not feasible. Lung ultrasound is a portable, easy to disinfect, low cost and non-invasive tool that can be used to identify lung diseases. Computer-assisted analysis of lung ultrasound imagery is a relatively recent approach that has shown great potential for diagnosing pulmonary conditions, being a viable alternative for screening and diagnosing COVID-19. Objective To evaluate and compare the performance of deep-learning techniques for detecting COVID-19 infections from lung ultrasound imagery. Methods We adapted different pre-trained deep learning architectures, including VGG19, InceptionV3, Xception, and ResNet50. We used the publicly available POCUS dataset comprising 3326 lung ultrasound frames of healthy, COVID-19, and pneumonia patients for training and fine-tuning. We conducted two experiments considering three classes (COVID-19, pneumonia, and healthy) and two classes (COVID-19 versus pneumonia and COVID-19 versus non-COVID-19) of predictive models. The obtained results were also compared with the POCOVID-net model. For performance evaluation, we calculated per-class classification metrics (Precision, Recall, and F1-score) and overall metrics (Accuracy, Balanced Accuracy, and Area Under the Receiver Operating Characteristic Curve). Lastly, we performed a statistical analysis of performance results using ANOVA and Friedman tests followed by post-hoc analysis using the Wilcoxon signed-rank test with the Holm’s step-down correction. Results InceptionV3 network achieved the best average accuracy (89.1%), balanced accuracy (89.3%), and area under the receiver operating curve (97.1%) for COVID-19 detection from bacterial pneumonia and healthy lung ultrasound data. The ANOVA and Friedman tests found statistically significant performance differences between models for accuracy, balanced accuracy and area under the receiver operating curve. Post-hoc analysis showed statistically significant differences between the performance obtained with the InceptionV3-based model and POCOVID-net, VGG19-, and ResNet50-based models. No statistically significant differences were found in the performance obtained with InceptionV3- and Xception-based models. Conclusions Deep learning techniques for computer-assisted analysis of lung ultrasound imagery provide a promising avenue for COVID-19 screening and diagnosis. Particularly, we found that the InceptionV3 network provides the most promising predictive results from all AI-based techniques evaluated in this work. InceptionV3- and Xception-based models can be used to further develop a viable computer-assisted screening tool for COVID-19 based on ultrasound imagery.

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A novel framework for rapid diagnosis of COVID-19 on computed tomography scans

Cited by 75 publications

References 37 publications

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches

Deep-learning based detection of COVID-19 using lung ultrasound imagery

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