Convolutional Neural Network Model to Detect COVID-19 Patients Utilizing Chest X-Ray Images. Satu, M. S., Ahammed, K., Abedin, M. Z., Rahman, M. A., Islam, S. M. S., Azad, A. K. M., Alyami, S. A., & Moni, M. A. In Satu, M. S., Moni, M. A., Kaiser, M. S., & Arefin, M. S., editors, Machine Intelligence and Emerging Technologies, pages 152–166, Cham, 2023. Springer Nature Switzerland. doi abstract bibtex This study aims to propose a deep learning model and detect COVID-19 chest X-ray cases more precisely. We have merged all the publicly available chest X-ray datasets of COVID-19 infected patients from Kaggle and Github, and pre-processed it using random sampling. Then, we proposed an enhanced convolutional neural network (CNN) model to this dataset and obtained a 94.03% accuracy, 95.52% AUC and 94.03% f-measure for detecting COVID-19 patients. We have also performed a comparative performance between proposed CNN model with several state-of-the-art classifiers including support vector machine, random forest, k-nearest neighbor, logistic regression, gaussian naïve bayes, bernoulli naïve bayes, decision tree, Xgboost, multilayer perceptron, nearest centroid, perceptron, deep neural network and pre-trained models such as residual neural network 50, visual geometry group network 16, and inception network V3 were employed, where our model yielded outperforming results compared to all other models. While evaluating the performance of our models, we have emphasized on specificity along with accuracy to identify non-COVID-19 individuals more accurately, which may potentially facilitate the early detection of COVID-19 patients for their preliminary screening, especially in under-resourced health infrastructure with insufficient PCR testing systems and testing facilities. This model could also be applicable to cases of other lung infections.
@inproceedings{satu_convolutional_2023,
address = {Cham},
title = {Convolutional {Neural} {Network} {Model} to {Detect} {COVID}-19 {Patients} {Utilizing} {Chest} {X}-{Ray} {Images}},
isbn = {978-3-031-34619-4},
doi = {10.1007/978-3-031-34619-4_13},
abstract = {This study aims to propose a deep learning model and detect COVID-19 chest X-ray cases more precisely. We have merged all the publicly available chest X-ray datasets of COVID-19 infected patients from Kaggle and Github, and pre-processed it using random sampling. Then, we proposed an enhanced convolutional neural network (CNN) model to this dataset and obtained a 94.03\% accuracy, 95.52\% AUC and 94.03\% f-measure for detecting COVID-19 patients. We have also performed a comparative performance between proposed CNN model with several state-of-the-art classifiers including support vector machine, random forest, k-nearest neighbor, logistic regression, gaussian naïve bayes, bernoulli naïve bayes, decision tree, Xgboost, multilayer perceptron, nearest centroid, perceptron, deep neural network and pre-trained models such as residual neural network 50, visual geometry group network 16, and inception network V3 were employed, where our model yielded outperforming results compared to all other models. While evaluating the performance of our models, we have emphasized on specificity along with accuracy to identify non-COVID-19 individuals more accurately, which may potentially facilitate the early detection of COVID-19 patients for their preliminary screening, especially in under-resourced health infrastructure with insufficient PCR testing systems and testing facilities. This model could also be applicable to cases of other lung infections.},
language = {en},
booktitle = {Machine {Intelligence} and {Emerging} {Technologies}},
publisher = {Springer Nature Switzerland},
author = {Satu, Md. Shahriare and Ahammed, Khair and Abedin, Mohammad Zoynul and Rahman, Md. Auhidur and Islam, Sheikh Mohammed Shariful and Azad, A. K. M. and Alyami, Salem A. and Moni, Mohammad Ali},
editor = {Satu, Md. Shahriare and Moni, Mohammad Ali and Kaiser, M. Shamim and Arefin, Mohammad Shamsul},
year = {2023},
keywords = {Chest-Xray Images, Convolutional Neural Network, COVID-19, Deep Learning, Machine Learning},
pages = {152--166},
}
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Then, we proposed an enhanced convolutional neural network (CNN) model to this dataset and obtained a 94.03% accuracy, 95.52% AUC and 94.03% f-measure for detecting COVID-19 patients. We have also performed a comparative performance between proposed CNN model with several state-of-the-art classifiers including support vector machine, random forest, k-nearest neighbor, logistic regression, gaussian naïve bayes, bernoulli naïve bayes, decision tree, Xgboost, multilayer perceptron, nearest centroid, perceptron, deep neural network and pre-trained models such as residual neural network 50, visual geometry group network 16, and inception network V3 were employed, where our model yielded outperforming results compared to all other models. 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