An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images
COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/21/10301 |
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| author | Muhammad Shoaib Farooq Attique Ur Rehman Muhammad Idrees Muhammad Ahsan Raza Jehad Ali Mehedi Masud Jehad F. Al-Amri Syed Hasnain Raza Kazmi |
| author_facet | Muhammad Shoaib Farooq Attique Ur Rehman Muhammad Idrees Muhammad Ahsan Raza Jehad Ali Mehedi Masud Jehad F. Al-Amri Syed Hasnain Raza Kazmi |
| author_sort | Muhammad Shoaib Farooq |
| collection | DOAJ |
| description | COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak. |
| first_indexed | 2024-03-10T06:06:56Z |
| format | Article |
| id | doaj.art-cddd60ad1caf4c4d80260d2ec3e38893 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T06:06:56Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-cddd60ad1caf4c4d80260d2ec3e388932023-11-22T20:30:48ZengMDPI AGApplied Sciences2076-34172021-11-0111211030110.3390/app112110301An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray ImagesMuhammad Shoaib Farooq0Attique Ur Rehman1Muhammad Idrees2Muhammad Ahsan Raza3Jehad Ali4Mehedi Masud5Jehad F. Al-Amri6Syed Hasnain Raza Kazmi7Department of Computer Science, School of System and Technology, University of Management and Technology, Lahore 54000, PakistanDepartment of Computer Science, Lahore Garrison University, Lahore 54000, PakistanDepartment of Computer Science and Engineering, UET Lahore, Narowal Campus, Lahore 54890, PakistanDepartment of Information Technology, Bahauddin Zakariya University, Multan 60000, PakistanDepartment of Computer Engineering, and Department of AI Convergence Network, Ajou University, Suwon 16499, KoreaDepartment of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Computer Science, School of System and Technology, University of Management and Technology, Lahore 54000, PakistanCOVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.https://www.mdpi.com/2076-3417/11/21/10301convolutionalCOVID-19neural networkchest X-raymodeldetection |
| spellingShingle | Muhammad Shoaib Farooq Attique Ur Rehman Muhammad Idrees Muhammad Ahsan Raza Jehad Ali Mehedi Masud Jehad F. Al-Amri Syed Hasnain Raza Kazmi An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images Applied Sciences convolutional COVID-19 neural network chest X-ray model detection |
| title | An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images |
| title_full | An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images |
| title_fullStr | An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images |
| title_full_unstemmed | An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images |
| title_short | An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images |
| title_sort | effective convolutional neural network model for the early detection of covid 19 using chest x ray images |
| topic | convolutional COVID-19 neural network chest X-ray model detection |
| url | https://www.mdpi.com/2076-3417/11/21/10301 |
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