COVID-19 Detection from X-ray Images Using Different Artificial Intelligence Hybrid Models

Alqudah, Ali Mohammad; Qazan, Shoroq; Alquran, Hiam; Qasmieh, Isam Abu; Alqudah, Amin

doi:10.5455/jjee.204-1585312246

Cited by 90 publications

(92 citation statements)

References 15 publications

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“…From Table 5 , it is found that some of the proposed systems [ 22 , 28 , 31 , 31 , [53] , [54] , [55] ], and [ 56 ] obtained a slightly lower accuracy in range of 80.6%–92.3%. The moderately highest accuracy of 93.5%, 95.2%, 95.4%, 98.3% and 98.3% are found in [ 23 , 26 , 29 , 57 ], and [ 25 ] respectively. The system developed in [ 58 ] obtained an overall accuracy of 98.08% considering the multi-classes.…”

Section: Discussionmentioning

confidence: 99%

“…The system developed in [ 58 ] obtained an overall accuracy of 98.08% considering the multi-classes. Moreover, a comparison between existing systems in terms of computational time depicted that the system developed in [ 23 ] required 6.3s to classify 21 test images [ 25 ], needed 2277.6s for 8997 training images [ 31 ], took 2641.0s and 4.0s for training and testing of 40 and 10 images respectively, and [ 56 ] consumed 79184.3s and 262.0s for training and testing of 4449 and 1638 images respectively. In our experiment, the CNN architecture required 18950.0s and 114.0s for training and testing 3660 and 915 images respectively.…”

Section: Discussionmentioning

confidence: 99%

“…The experimental outcome obtained 99.56% accuracy and 80.53% recall for COVID-19 cases. Alqudah et al [ 23 ] used artificial intelligence techniques to develop a system that detected COVID-19 from chest X-rays. The images used were classified with different machine learning techniques, such as support vector machine (SVM), CNN, and random forest (RF).…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Islam

Asraf

2020

Informatics in Medicine Unlocked

551

278

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Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An automatic disease detection framework assists doctors in the diagnosis of disease and provides exact, consistent, and fast results and reduces the death rate. Coronavirus (COVID-19) has become one of the most severe and acute diseases in recent times and has spread globally. Therefore, an automated detection system, as the fastest diagnostic option, should be implemented to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 4575 X-ray images, including 1525 images of COVID-19, were used as a dataset in this system. The experimental results show that our proposed system achieved an accuracy of 99.4%, AUC of 99.9%, specificity of 99.2%, sensitivity of 99.3%, and F1-score of 98.9%. The system achieved desired results on the currently available dataset, which can be further improved when more COVID-19 images become available. The proposed system can help doctors to diagnose and treat COVID-19 patients easily.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Islam

Asraf

2020

Informatics in Medicine Unlocked

551

278

View full text Add to dashboard Cite

show abstract

“…An accuracy of 98.3% was obtained from the SqueezeNet model. Alqudah et al 21 implemented a hybrid system for distinguishing the COVID-19 and non-COVID-19 patients. The hybrid system utilized CNN and machine learning techniques such as SVM and random forest (RF).…”

Section: Related Workmentioning

confidence: 99%

Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images

Singh

Kumar

Yadav

et al. 2020

Int. J. Patt. Recogn. Artif. Intell.

101

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There are limited coronavirus disease 2019 (COVID-19) testing kits, therefore, development of other diagnosis approaches is desirable. The doctors generally utilize chest X-rays and Computed Tomography (CT) scans to diagnose pneumonia, lung inflammation, abscesses, and/or enlarged lymph nodes. Since COVID-19 attacks the epithelial cells that line our respiratory tract, therefore, X-ray images are utilized in this paper, to classify the patients with infected (COVID-19 [Formula: see text]ve) and uninfected (COVID-19 [Formula: see text]ve) lungs. Almost all hospitals have X-ray imaging machines, therefore, the chest X-ray images can be used to test for COVID-19 without utilizing any kind of dedicated test kits. However, the chest X-ray-based COVID-19 classification requires a radiology expert and significant time, which is precious when COVID-19 infection is increasing at a rapid rate. Therefore, the development of an automated analysis approach is desirable to save the medical professionals’ valuable time. In this paper, a deep convolutional neural network (CNN) approach is designed and implemented. Besides, the hyper-parameters of CNN are tuned using Multi-objective Adaptive Differential Evolution (MADE). Extensive experiments are performed by considering the benchmark COVID-19 dataset. Comparative analysis reveals that the proposed technique outperforms the competitive machine learning models in terms of various performance metrics.

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“…Given these advantages of X-ray imaging, many researchers have exerted efforts to find an accurate COVID-19 detection tool using chest X-ray images [7][8][9]. Researchers in [10] used artificial intelligence (AI) techniques in the early detection of COVID19 using chest X-ray images. These images were classified using several machine learning algorithms, such as support vector machine (SVM), convolutional neural network (CNN), and random forest (RF).…”

Section: Introductionmentioning

confidence: 99%

Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

et al. 2020

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The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

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COVID-19 Detection from X-ray Images Using Different Artificial Intelligence Hybrid Models

Cited by 90 publications

References 15 publications

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images

Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

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