COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images
The COVID-19 pandemic caused by the new coronavirus SARS-CoV-2 has changed the world as we know it. An early diagnosis is crucial in order to prevent new outbreaks and control its rapid spread. Medical imaging techniques, such as X-ray or chest computed tomography, are commonly used for this purpose...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/16/5683 |
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| author | Lourdes Duran-Lopez Juan Pedro Dominguez-Morales Jesús Corral-Jaime Saturnino Vicente-Diaz Alejandro Linares-Barranco |
| author_facet | Lourdes Duran-Lopez Juan Pedro Dominguez-Morales Jesús Corral-Jaime Saturnino Vicente-Diaz Alejandro Linares-Barranco |
| author_sort | Lourdes Duran-Lopez |
| collection | DOAJ |
| description | The COVID-19 pandemic caused by the new coronavirus SARS-CoV-2 has changed the world as we know it. An early diagnosis is crucial in order to prevent new outbreaks and control its rapid spread. Medical imaging techniques, such as X-ray or chest computed tomography, are commonly used for this purpose due to their reliability for COVID-19 diagnosis. Computer-aided diagnosis systems could play an essential role in aiding radiologists in the screening process. In this work, a novel Deep Learning-based system, called COVID-XNet, is presented for COVID-19 diagnosis in chest X-ray images. The proposed system performs a set of preprocessing algorithms to the input images for variability reduction and contrast enhancement, which are then fed to a custom Convolutional Neural Network in order to extract relevant features and perform the classification between COVID-19 and normal cases. The system is trained and validated using a 5-fold cross-validation scheme, achieving an average accuracy of 94.43% and an AUC of 0.988. The output of the system can be visualized using Class Activation Maps, highlighting the main findings for COVID-19 in X-ray images. These promising results indicate that COVID-XNet could be used as a tool to aid radiologists and contribute to the fight against COVID-19. |
| first_indexed | 2024-03-10T17:22:14Z |
| format | Article |
| id | doaj.art-30893f57fedb47a584962f755dac24b7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T17:22:14Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-30893f57fedb47a584962f755dac24b72023-11-20T10:18:41ZengMDPI AGApplied Sciences2076-34172020-08-011016568310.3390/app10165683COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray ImagesLourdes Duran-Lopez0Juan Pedro Dominguez-Morales1Jesús Corral-Jaime2Saturnino Vicente-Diaz3Alejandro Linares-Barranco4Robotics and Tech. of Computers Lab, ETSII-EPS, Universidad de Sevilla, 41011 Seville, SpainRobotics and Tech. of Computers Lab, ETSII-EPS, Universidad de Sevilla, 41011 Seville, SpainServicio de Oncología Médica, Clinica Universidad de Navarra, 28027 Madrid, SpainRobotics and Tech. of Computers Lab, ETSII-EPS, Universidad de Sevilla, 41011 Seville, SpainRobotics and Tech. of Computers Lab, ETSII-EPS, Universidad de Sevilla, 41011 Seville, SpainThe COVID-19 pandemic caused by the new coronavirus SARS-CoV-2 has changed the world as we know it. An early diagnosis is crucial in order to prevent new outbreaks and control its rapid spread. Medical imaging techniques, such as X-ray or chest computed tomography, are commonly used for this purpose due to their reliability for COVID-19 diagnosis. Computer-aided diagnosis systems could play an essential role in aiding radiologists in the screening process. In this work, a novel Deep Learning-based system, called COVID-XNet, is presented for COVID-19 diagnosis in chest X-ray images. The proposed system performs a set of preprocessing algorithms to the input images for variability reduction and contrast enhancement, which are then fed to a custom Convolutional Neural Network in order to extract relevant features and perform the classification between COVID-19 and normal cases. The system is trained and validated using a 5-fold cross-validation scheme, achieving an average accuracy of 94.43% and an AUC of 0.988. The output of the system can be visualized using Class Activation Maps, highlighting the main findings for COVID-19 in X-ray images. These promising results indicate that COVID-XNet could be used as a tool to aid radiologists and contribute to the fight against COVID-19.https://www.mdpi.com/2076-3417/10/16/5683COVID-19deep learningconvolutional neural networksmedical image analysiscomputer-aided diagnosisX-ray |
| spellingShingle | Lourdes Duran-Lopez Juan Pedro Dominguez-Morales Jesús Corral-Jaime Saturnino Vicente-Diaz Alejandro Linares-Barranco COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images Applied Sciences COVID-19 deep learning convolutional neural networks medical image analysis computer-aided diagnosis X-ray |
| title | COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images |
| title_full | COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images |
| title_fullStr | COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images |
| title_full_unstemmed | COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images |
| title_short | COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images |
| title_sort | covid xnet a custom deep learning system to diagnose and locate covid 19 in chest x ray images |
| topic | COVID-19 deep learning convolutional neural networks medical image analysis computer-aided diagnosis X-ray |
| url | https://www.mdpi.com/2076-3417/10/16/5683 |
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