“Fast deep learning computer-aided diagnosis of COVID-19 based on digital chest x-ray images”

Al-antari, Mugahed A.; Hua, Cam-Hao; Bang, Jae Hun; Lee, Sungyoung

doi:10.1007/s10489-020-02076-6

Cited by 86 publications

(45 citation statements)

References 32 publications

(97 reference statements)

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“…Al-antari et al [10] presented a simultaneous deep learning computer-aided diagnostic tool developed and based on the YOLO predictor for detecting and diagnosing COVID-19 lung disease from the entire chest X-ray images. Their model was evaluated through five-fold tests for multi-class prediction problem by using two different chest X-ray images.…”

Section: Related Workmentioning

confidence: 99%

CovFrameNet: An Enhanced Deep Learning Framework for COVID-19 Detection

2021

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The novel coronavirus, also known as COVID-19, is a pandemic that has weighed heavily on the socio-economic affairs of the world. Research into the production of relevant vaccines is progressively being advanced with the development of the Pfizer and BioNTech, AstraZeneca, Moderna, Sputnik V, Janssen, Sinopharm, Valneva, Novavax and Sanofi Pasteur vaccines. There is, however, a need for a computational intelligence solution approach to mediate the process of facilitating quick detection of the disease. Different computational intelligence methods, which comprise natural language processing, knowledge engineering, and deep learning, have been proposed in the literature to tackle the spread of coronavirus disease. More so, the application of deep learning models have demonstrated an impressive performance compared to other methods. This paper aims to advance the application of deep learning and image pre-processing techniques to characterise and detect novel coronavirus infection. Furthermore, the study proposes a framework named CovFrameNet., which consist of a pipelined image pre-processing method and a deep learning model for feature extraction, classification, and performance measurement. The novelty of this study lies in the design of a CNN architecture that incorporates an enhanced image preprocessing mechanism. The National Institutes of Health (NIH) Chest X-Ray dataset and COVID-19 Radiography database were used to evaluate and validate the effectiveness of the proposed deep learning model. Results obtained revealed that the proposed model achieved an accuracy of 0.1, recall/precision of 0.85, F-measure of 0.9, and specificity of 1.0. Thus, the study's outcome showed that a CNN-based method with image pre-processing capability could be adopted for the pre-screening of suspected COVID-19 cases, and the confirmation of RT-PCR-based detected cases of COVID-19.

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Section: Related Workmentioning

confidence: 99%

CovFrameNet: An Enhanced Deep Learning Framework for COVID-19 Detection

2021

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“…Using a plain inception network, this method was able to produce some promising results with an accuracy over 80%. Al-antari et al [16] predicted COVID-19 cases from digital chest X-ray images using a computer-aided diagnose network based on the YOLO predictor [17]. The network consists of a CNN-based deep feature extraction followed by fully connected layers that then gives a confidence score that can tell if the X-ray belongs to the COVID-19 class.…”

Section: Related Workmentioning

confidence: 99%

CORONA-Net: Diagnosing COVID-19 from X-ray Images Using Re-Initialization and Classification Networks

et al. 2021

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The COVID-19 pandemic has been deemed a global health pandemic. The early detection of COVID-19 is key to combating its outbreak and could help bring this pandemic to an end. One of the biggest challenges in combating COVID-19 is accurate testing for the disease. Utilizing the power of Convolutional Neural Networks (CNNs) to detect COVID-19 from chest X-ray images can help radiologists compare and validate their results with an automated system. In this paper, we propose a carefully designed network, dubbed CORONA-Net, that can accurately detect COVID-19 from chest X-ray images. CORONA-Net is divided into two phases: (1) The reinitialization phase and (2) the classification phase. In the reinitialization phase, the network consists of encoder and decoder networks. The objective of this phase is to train and initialize the encoder and decoder networks by a distribution that comes out of medical images. In the classification phase, the decoder network is removed from CORONA-Net, and the encoder network acts as a backbone network to fine-tune the classification phase based on the learned weights from the reinitialization phase. Extensive experiments were performed on a publicly available dataset, COVIDx, and the results show that CORONA-Net significantly outperforms the current state-of-the-art networks with an overall accuracy of 95.84%.

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“…The RBF has two inputs, which are the coordinates of a point, and a single output in the approximation situation of a 2-variable function. The phrase is used to calculate the RBF output with the input A= [a 1, a 2 ] T in equation (3). A Gaussian function is used as an RBF, which, in its two-dimensional case, is written as:…”

Section: Figure 7 Radial Basis Function Workingmentioning

confidence: 99%

Detection and Classification of COVID-19 Using Machine Learning

Shahin

Alshammari

Taloba

et al. 2021

Preprint

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As people all over the world are vulnerable to be affected by the COVID-19 virus, the automatic detection of such a virus is an important concern. The paper aims to detect and classify coronavirus using machine learning. To spot and identify coronavirus in CT-Lung screening and Computer-Aided diagnosis (CAD) system is projected to distinguish and classifies the COVID-19. By utilizing the clinical specimens obtained from the corona-infected patients with the help of some machine learning techniques like Decision Tree, Support Vector Machine, K-means clustering, and Radial Basis Function. While some specialists believe that the RT-PCR test is the best option for diagnosing Covid-19 patients, others believe that CT scans of the lungs can be more accurate in diagnosing coronavirus infection, as well as being less expensive than the PCR test. The clinical specimens include serum specimens, respiratory secretions, and whole blood specimens. Overall, 15 factors are measured from these specimens as the result of the previous clinical examinations. The proposed CAD system consists of four phases starting with the CT lungs screening collection, followed by a pre-processing stage to enhance the appearance of the ground glass opacities (GGOs) nodules as they originally lock hazy with fainting contrast. A modified K-means algorithm will be used to detect and segment these regions. Finally, we will use the detected, infected areas that obtained in the detection phase with a scale of 50x50 and we will crop the solid false positives that seem to be GGOs as inputs and targets for the machine learning classifiers, here we will use a support vector machine (SVM) and Radial basis function (RBF). Moreover, a GUI application is developed which avoids the confusion of the doctors for getting the exact results by giving the 15 input factors obtained from the clinical specimens.

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“Fast deep learning computer-aided diagnosis of COVID-19 based on digital chest x-ray images”

Cited by 86 publications

References 32 publications

CovFrameNet: An Enhanced Deep Learning Framework for COVID-19 Detection

CovFrameNet: An Enhanced Deep Learning Framework for COVID-19 Detection

CORONA-Net: Diagnosing COVID-19 from X-ray Images Using Re-Initialization and Classification Networks

Detection and Classification of COVID-19 Using Machine Learning

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