CRV-NET: Robust Intensity Recognition of Coronavirus in Lung Computerized Tomography Scan Images
The early diagnosis of infectious diseases is demanded by digital healthcare systems. Currently, the detection of the new coronavirus disease (COVID-19) is a major clinical requirement. For COVID-19 detection, deep learning models are used in various studies, but the robustness is still compromised....
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/10/1783 |
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| author | Uzair Iqbal Romil Imtiaz Abdul Khader Jilani Saudagar Khubaib Amjad Alam |
| author_facet | Uzair Iqbal Romil Imtiaz Abdul Khader Jilani Saudagar Khubaib Amjad Alam |
| author_sort | Uzair Iqbal |
| collection | DOAJ |
| description | The early diagnosis of infectious diseases is demanded by digital healthcare systems. Currently, the detection of the new coronavirus disease (COVID-19) is a major clinical requirement. For COVID-19 detection, deep learning models are used in various studies, but the robustness is still compromised. In recent years, deep learning models have increased in popularity in almost every area, particularly in medical image processing and analysis. The visualization of the human body’s internal structure is critical in medical analysis; many imaging techniques are in use to perform this job. A computerized tomography (CT) scan is one of them, and it has been generally used for the non-invasive observation of the human body. The development of an automatic segmentation method for lung CT scans showing COVID-19 can save experts time and can reduce human error. In this article, the CRV-NET is proposed for the robust detection of COVID-19 in lung CT scan images. A public dataset (SARS-CoV-2 CT Scan dataset), is used for the experimental work and customized according to the scenario of the proposed model. The proposed modified deep-learning-based U-Net model is trained on a custom dataset with 221 training images and their ground truth, which was labeled by an expert. The proposed model is tested on 100 test images, and the results show that the model segments COVID-19 with a satisfactory level of accuracy. Moreover, the comparison of the proposed CRV-NET with different state-of-the-art convolutional neural network models (CNNs), including the U-Net Model, shows better results in terms of accuracy (96.67%) and robustness (low epoch value in detection and the smallest training data size). |
| first_indexed | 2024-03-11T03:47:46Z |
| format | Article |
| id | doaj.art-7295052ceeed40ba8e23e99edebf8710 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T03:47:46Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-7295052ceeed40ba8e23e99edebf87102023-11-18T01:05:08ZengMDPI AGDiagnostics2075-44182023-05-011310178310.3390/diagnostics13101783CRV-NET: Robust Intensity Recognition of Coronavirus in Lung Computerized Tomography Scan ImagesUzair Iqbal0Romil Imtiaz1Abdul Khader Jilani Saudagar2Khubaib Amjad Alam3Department of Artificial Intelligence and Data Science, National University of Computer and Emerging Sciences, Islamabad Campus, Islamabad 44000, PakistanInformation and Communication Engineering, Northwestern Polytechnical University, Xi’an 710072, ChinaInformation Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi ArabiaDepartment of Software Engineering, National University of Computer and Emerging Sciences, Islamabad Campus, Islamabad 44000, PakistanThe early diagnosis of infectious diseases is demanded by digital healthcare systems. Currently, the detection of the new coronavirus disease (COVID-19) is a major clinical requirement. For COVID-19 detection, deep learning models are used in various studies, but the robustness is still compromised. In recent years, deep learning models have increased in popularity in almost every area, particularly in medical image processing and analysis. The visualization of the human body’s internal structure is critical in medical analysis; many imaging techniques are in use to perform this job. A computerized tomography (CT) scan is one of them, and it has been generally used for the non-invasive observation of the human body. The development of an automatic segmentation method for lung CT scans showing COVID-19 can save experts time and can reduce human error. In this article, the CRV-NET is proposed for the robust detection of COVID-19 in lung CT scan images. A public dataset (SARS-CoV-2 CT Scan dataset), is used for the experimental work and customized according to the scenario of the proposed model. The proposed modified deep-learning-based U-Net model is trained on a custom dataset with 221 training images and their ground truth, which was labeled by an expert. The proposed model is tested on 100 test images, and the results show that the model segments COVID-19 with a satisfactory level of accuracy. Moreover, the comparison of the proposed CRV-NET with different state-of-the-art convolutional neural network models (CNNs), including the U-Net Model, shows better results in terms of accuracy (96.67%) and robustness (low epoch value in detection and the smallest training data size).https://www.mdpi.com/2075-4418/13/10/1783deep learningmachine learningcomputerized tomographyconvolutional neural networkU-Net |
| spellingShingle | Uzair Iqbal Romil Imtiaz Abdul Khader Jilani Saudagar Khubaib Amjad Alam CRV-NET: Robust Intensity Recognition of Coronavirus in Lung Computerized Tomography Scan Images Diagnostics deep learning machine learning computerized tomography convolutional neural network U-Net |
| title | CRV-NET: Robust Intensity Recognition of Coronavirus in Lung Computerized Tomography Scan Images |
| title_full | CRV-NET: Robust Intensity Recognition of Coronavirus in Lung Computerized Tomography Scan Images |
| title_fullStr | CRV-NET: Robust Intensity Recognition of Coronavirus in Lung Computerized Tomography Scan Images |
| title_full_unstemmed | CRV-NET: Robust Intensity Recognition of Coronavirus in Lung Computerized Tomography Scan Images |
| title_short | CRV-NET: Robust Intensity Recognition of Coronavirus in Lung Computerized Tomography Scan Images |
| title_sort | crv net robust intensity recognition of coronavirus in lung computerized tomography scan images |
| topic | deep learning machine learning computerized tomography convolutional neural network U-Net |
| url | https://www.mdpi.com/2075-4418/13/10/1783 |
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