Prevođenje detekcije COVID-19 u kontinuirani problem predviđanja: Pristup zasnovan na regresiji koristeći mašinsko učenje
Sažetak
The COVID-19 pandemic has highlighted the urgent need for accurate and reliable methods for early detection and management of cases. In this study, we present a novel approach for COVID-19 case prediction using chest radiography images. Our method involves converting the classification task into a regression task, which enables improved accuracy and robustness in the model. We performed both internal and external validation, with train =0.91, CV-MSE=0.0253, and =0.91, indicating high accuracy and reliability in predicting COVID-19 cases. Additionally, we conducted an applicability domain analysis, which showed that 99% of unseen data can be accurately predicted by our model. Our findings suggest that our method can be a valuable tool in the early detection and management of COVID-19 cases, which can ultimately improve patient outcomes and public health. Further validation and testing in real-world clinical settings are needed to confirm the effectiveness and generalizability of our approach.
Reference
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4. Kopel J, Goyal H, Perisetti A. Antibody tests for COVID-19 [abstract].34;2021. 63-72P.
5. Nasiri H, Hasani S. Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost. Radiography 2022;28:732-8.
6. Das NN, Kumar N, Kaur M, Kumar V, Singh D. Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays. Irbm 2022;43:114-9.
7. Wang L, Lin ZQ, Wong A. Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific reports 2020;10:1-12.
8. Hemdan EE-D, Shouman MA, Karar ME. Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv preprint arXiv:200311055 2020.
9. Hou J, Gao T. Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection. Scientific Reports 2021;11:1-15.
10. Gao T, Wang G. Chest X-ray image analysis and classification for COVID-19 pneumonia detection using Deep CNN. medRxiv 2020:2020.08. 20.20178913.
11. Alqahtani A, Zahoor MM, Nasrullah R, Fareed A, Cheema AA, Shahrose A, et al. Computer Aided COVID-19 Diagnosis in Pandemic Era Using CNN in Chest X-ray Images. Life 2022;12:1709.
12. Carlile M, Hurt B, Hsiao A, Hogarth M, Longhurst CA, Dameff C. Deployment of artificial intelligence for radiographic diagnosis of COVID‐19 pneumonia in the emergency department. Journal of the American College of Emergency Physicians Open 2020;1:1459-64.
13. Farooq M, Hafeez A. Covid-resnet: A deep learning framework for screening of covid19 from radiographs. arXiv preprint arXiv:200314395 2020.
14. Abbas A, Abdelsamea MM, Gaber MM. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Applied Intelligence 2021;51:854-64.
15. Mukherjee H, Ghosh S, Dhar A, Obaidullah SM, Santosh K, Roy K. Shallow convolutional neural network for COVID-19 outbreak screening using chest X-rays. Cognitive Computation 2021:1-14.
16. Alif Rahman (2020). COVID-19 Chest X-ray Image Dataset. Retrieved january 20, 2023 from https://www.kaggle.com/datasets/alifrahman/covid19-chest-xray-image-dataset
17. Abd Almisreb A, Jamil N, Din NM. Utilizing AlexNet deep transfer learning for ear recognition [abstract].2018. 1-5P.
18. Rocha M, Cortez P, Neves J. Evolution of neural networks for classification and regression. Neurocomputing 2007;70:2809-16.
19. Bebis G, Georgiopoulos M. Feed-forward neural networks. Ieee Potentials 1994;13:27-31.
20. Khaouane A, Ferhat S, Hanini S. A Novel Methodology for Human Plasma Protein Binding: Prediction, Validation, and Applicability Domain. Pharmaceutical and Biomedical Research 2022;8:311-22.
21. Alexander DL, Tropsha A, Winkler DA. Beware of R 2: simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models. Journal of chemical information and modeling 2015;55:1316-22.
22. Roy K, Kar S, Ambure P. On a simple approach for determining applicability domain of QSAR models. Chemometrics and Intelligent Laboratory Systems 2015;145:22-9.
23. OECD. Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models.(2014).
Objavljeno
2025/11/19
Broj časopisa
Rubrika
Originalni rad / Original article