JayaLakshmi, G, Khalaf, Haitham, Farhadi, Abolfazl, Al Barzinji, Shokhan, Mahmood, Sawsan, Najim, Saif Al-din, Hutaihit, Maha, Nejrs, Salwa, Al Mahdawi, Raghda, Abdulbaqi, Azmi. (1400). Detection of COVID-19 from radiology modalities and identification of prognosis patterns. هنرهای کاربردی, 13(1), 1351-1365. doi: 10.22075/ijnaa.2022.5713
G JayaLakshmi; Haitham Abbas Khalaf; Abolfazl Farhadi; Shokhan M Al Barzinji; Sawsan dheyaa Mahmood; Saif Al-din M Najim; Maha A Hutaihit; Salwa Mohammed Nejrs; Raghda Salam Al Mahdawi; Azmi Shawkat Abdulbaqi. "Detection of COVID-19 from radiology modalities and identification of prognosis patterns". هنرهای کاربردی, 13, 1, 1400, 1351-1365. doi: 10.22075/ijnaa.2022.5713
JayaLakshmi, G, Khalaf, Haitham, Farhadi, Abolfazl, Al Barzinji, Shokhan, Mahmood, Sawsan, Najim, Saif Al-din, Hutaihit, Maha, Nejrs, Salwa, Al Mahdawi, Raghda, Abdulbaqi, Azmi. (1400). 'Detection of COVID-19 from radiology modalities and identification of prognosis patterns', هنرهای کاربردی, 13(1), pp. 1351-1365. doi: 10.22075/ijnaa.2022.5713
JayaLakshmi, G, Khalaf, Haitham, Farhadi, Abolfazl, Al Barzinji, Shokhan, Mahmood, Sawsan, Najim, Saif Al-din, Hutaihit, Maha, Nejrs, Salwa, Al Mahdawi, Raghda, Abdulbaqi, Azmi. Detection of COVID-19 from radiology modalities and identification of prognosis patterns. هنرهای کاربردی, 1400; 13(1): 1351-1365. doi: 10.22075/ijnaa.2022.5713

Detection of COVID-19 from radiology modalities and identification of prognosis patterns

International Journal of Nonlinear Analysis and Applications
مقاله 113، دوره 13، شماره 1، خرداد 2022، صفحه 1351-1365    اصل مقاله (7.88 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.5713
نویسندگان
G JayaLakshmi* 1؛ Haitham Abbas Khalaf2؛ Abolfazl Farhadi3؛ Shokhan M Al Barzinji4؛ Sawsan dheyaa Mahmood5؛ Saif Al-din M Najim4؛ Maha A Hutaihit6؛ Salwa Mohammed Nejrs7؛ Raghda Salam Al Mahdawi8؛ Azmi Shawkat Abdulbaqi4
1Department of IT, V R Siddhartha Engineering College, India
2College of Medicine, University of Anbar, Ramadi, Iraq
3Department of Nursing Sciences, Shirvan Branch, Islamic Azad University, Shirvan, Iran
4Department of Computer Science, College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq
5Department of Electricity Engineering College of Engineering, University of Tikrit, Tikrit, Iraq
6Department of Communication Engineering Collage of Engineering, University of Diyala: Baqubah, Diyala, Iraq
7Directorate of Private University Education, Ministry of Higher Education, Baghdad, Iraq
8Department of Computer Engineering Collage of Engineering, University of Diyala: Baqubah, Diyala, Iraq
چکیده
SARS-CoV-2 and the consequential COVID-19 virus is one of the major concerns of the 21st century. Pertaining to the novelty of the disease, it became necessary to discover the efficacy of deep learning techniques in the quick and consistent discovery of COVID-19 based on chest X-ray and CT scan image analysis. In this related work, Prognostic tool using regression was designed for patients with COVID-19 and recognizing prediction patterns to make available important prognostic information on mortality or severity in COVID-19 patients. And reliable convolutional neural network (CNN) architecture models (DenseNet, VGG16, ResNet, Inception Net)to institute whether it would work preeminent in terms of accuracy as well as efficiency with image datasets with Transfer Learning. CNN with Transfer Learning were functional to accomplish the involuntary recognition of COVID-19 from numerary chest X-ray and CT scan images. The experimental results emphasize that selected models, which is formerly broadly tuned through suitable parameters, executes in extensive levels of COVID-19 discovery against pneumonia or normal or lung opacity through the precision of up to 87% for X-Ray and 91% intended for CT scans.
کلیدواژه‌ها
convolutional neural network؛ transfer learning؛ COVID-19؛ X-ray؛ CTscan؛ deep learning
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