Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Network

Asif, Sohaib; Yi, Wenhui; Hou, Jin; Si, Jinhai

doi:10.1109/iccc51575.2020.9344870

Cited by 124 publications

(35 citation statements)

References 19 publications

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“…Their system attained a 89.6 % and 95% accuracy on classification for 4 and 3 classes, respectively. Another DCNN transfer learning-based pipeline by Asif et al [18] utilized Inception V3 for the detection of COVID-19 in infected patients using chest X-ray scans. The test data contained 864 scans for COVID-19, 1345 for viral pneumonia and 1341 for normal scans.…”

Section: Related Workmentioning

confidence: 99%

Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms

et al. 2022

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Accurate detection of COVID-19 is of immense importance to help physicians intervene with appropriate treatments. Although RT-PCR is routinely used for COVID-19 detection, it is expensive, takes a long time, and is prone to inaccurate results. Currently, medical imaging-based detection systems have been explored as an alternative for more accurate diagnosis. In this work, we propose a multi-level diagnostic framework for the accurate detection of COVID-19 using X-ray scans based on transfer learning. The developed framework consists of three stages, beginning with a pre-processing step to remove noise effects and image resizing followed by a deep learning architecture utilizing an Xception pre-trained model for feature extraction from the pre-processed image. Our design utilizes a global average pooling (GAP) layer for avoiding over-fitting, and an activation layer is added in order to reduce the losses. Final classification is achieved using a softmax layer. The system is evaluated using different activation functions and thresholds with different optimizers. We used a benchmark dataset from the kaggle website. The proposed model has been evaluated on 7395 images that consist of 3 classes (COVID-19, normal and pneumonia). Additionally, we compared our framework with the traditional pre-trained deep learning models and with other literature studies. Our evaluation using various metrics showed that our framework achieved a high test accuracy of 99.3% with a minimum loss of 0.02 using the LeakyReLU activation function at a threshold equal to 0.1 with the RMSprop optimizer. Additionally, we achieved a sensitivity and specificity of 99 and F1-Score of 99.3% with only 10 epochs and a 10−4 learning rate.

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Section: Related Workmentioning

confidence: 99%

Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms

et al. 2022

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“…These challenges have resorted data intensive modeling [6] and computational intelligence methods [7] that embrace the complexity of the issues that arise from the pandemic, from lack of human resources [8] and data resources [9,10], to the lack of emergency preparedness and capabilities to respond effectively [11]. An increasing amount of studies set out to explore models and artifacts that leverage artificial intelligence (AI) methods and methodologies to explore pandemic facts and circumstances from several differing yet often complementary angles, from the composites and overarching description of the virus itself [12], to diseases detection and diagnosis [13,14] to prediction on infection rates [15], patient management [16], the protection of healthcare workers [17,18], as well as hygiene measures, prevention and containment [19], drug development [20], and treatment [21][22][23]. The use of AI techniques is perceived to be a paradigm shift [24] towards approaches that use data science in empowering ways to craft, test and deploy public health care policies [25,26].…”

Section: Simulation Modeling Option and Artificial Intelligencementioning

confidence: 99%

Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data

et al. 2021

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Research on SARS-CoV-2 and its social implications have become a major focus to interdisciplinary teams worldwide. As interest in more direct solutions, such as mass testing and vaccination grows, several studies appear to be dedicated to the operationalization of those solutions, leveraging both traditional and new methodologies, and, increasingly, the combination of both. This research examines the challenges anticipated for preventative testing of SARS-CoV-2 in schools and proposes an artificial intelligence (AI)-powered agent-based model crafted specifically for school scenarios. This research shows that in the absence of real data, simulation-based data can be used to develop an artificial intelligence model for the application of rapid assessment of school testing policies.

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“…Something similar occurs with [7], whose purpose is to evaluate the identification of SARS-COV-2 with diagnostic tools such as pathogenic tests by name at the beginning of the "battle" against the transmission of said virus, emphasizing the mandatory detection of contaminated patients. On the other hand [8], it communicates in its problem the lack of access to test kits by pointing out the scope of SARS-COV-2 as openness and concerns about the accuracy of the counts of cases of this virus, focusing on the early stages of the pandemic concerning its scope, characteristics and its impact on health and society.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of Predictions through Machine Learning for Sars-Cov-2 Forecasting in Peru

Morales¹,

Antayhua²,

Andrade–Arenas³

2021

IJACSA

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The SARS-COV-2 virus of the coronavirus family was identified in 2019. This is a type of virus that infects humans and some animals, in Peru it has seriously affected everyone, causing so many deaths, which has resulted in that people be tested to rule out contagion, using laboratory methods recommended by the government of the country. Therefore, the data science methodology was used with this research, where its objective is to predict what types of people are contaminated during SARS-COV-2 by the regions of Peru, identified through laboratory methods, therefore, the "data bank" was taken by PNDA, the CSV file was used for that study, apart from the fact that it comes from the INS and the CDC of the MINSA. In which, machine learning was developed with the decision tree algorithm and then began coding, in such a way that the distribution called Anaconda was used where it is encoded in Python language, together with that distribution, Jupyter Notebook was used which is a client-server application. The results generated by this research prove that it was possible to identify the types of individuals by SARS-COV-2. These results can help prevention entities against SARS-COV-2 to apply the corresponding preventive measures in a more focused way.

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Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Network

Cited by 124 publications

References 19 publications

Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms

Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms

Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data

Development of Predictions through Machine Learning for Sars-Cov-2 Forecasting in Peru

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