Custom YOLO Object Detection Model for COVID-19 Diagnosis
The emergence and spread of the new coronavirus (COVID-19) poses a new public health threat to the entire world (SARS-CoV-2). This new virus is highly contagious and pathogenetically different from other mainstream respiratory viruses. Clinical staff can benefit from Computer Aided Diagnostics (CAD...
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| Format: | Article |
| Language: | English |
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middle technical university
2023-09-01
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| Series: | Journal of Techniques |
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| Online Access: | https://journal.mtu.edu.iq/index.php/MTU/article/view/1174 |
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| author | Noor Najah Ali Aseel Hameed Asanka G. Perera Ali Al_Naji |
| author_facet | Noor Najah Ali Aseel Hameed Asanka G. Perera Ali Al_Naji |
| author_sort | Noor Najah Ali |
| collection | DOAJ |
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The emergence and spread of the new coronavirus (COVID-19) poses a new public health threat to the entire world (SARS-CoV-2). This new virus is highly contagious and pathogenetically different from other mainstream respiratory viruses. Clinical staff can benefit from Computer Aided Diagnostics (CAD) systems that combine deep learning algorithms and image processing technologies as diagnostic tools for COVID-19. These tools also help to better understand the course of the disease. In most cases, medical staff and healthcare facilities would be more equipped to promptly diagnose COVID-19 for patients with improved flexibility. To examine the training performance of the contemporary YOLOv4 model, this work presents the development of a computer-assisted automatic detection system that focuses specifically on identifying viral cells in blood samples from patients using electron microscopy images to detect the infected blood cell. The mean average precision of the proposed custom model is 86.5%mAP, making it suitable for the upcoming COVID-19 monitoring systems.
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| format | Article |
| id | doaj-art-697876ad1c834da1a6398a70f418d418 |
| institution | Kabale University |
| issn | 1818-653X 2708-8383 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | middle technical university |
| record_format | Article |
| series | Journal of Techniques |
| spelling | doaj-art-697876ad1c834da1a6398a70f418d4182025-01-19T10:59:06Zengmiddle technical universityJournal of Techniques1818-653X2708-83832023-09-015310.51173/jt.v5i3.1174Custom YOLO Object Detection Model for COVID-19 DiagnosisNoor Najah Ali0Aseel Hameed1Asanka G. Perera2Ali Al_Naji3Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.University of New South Wales, Canberra, ACT 2610, AustraliaSchool of Engineering, University of South Australia, Adelaide, Australia The emergence and spread of the new coronavirus (COVID-19) poses a new public health threat to the entire world (SARS-CoV-2). This new virus is highly contagious and pathogenetically different from other mainstream respiratory viruses. Clinical staff can benefit from Computer Aided Diagnostics (CAD) systems that combine deep learning algorithms and image processing technologies as diagnostic tools for COVID-19. These tools also help to better understand the course of the disease. In most cases, medical staff and healthcare facilities would be more equipped to promptly diagnose COVID-19 for patients with improved flexibility. To examine the training performance of the contemporary YOLOv4 model, this work presents the development of a computer-assisted automatic detection system that focuses specifically on identifying viral cells in blood samples from patients using electron microscopy images to detect the infected blood cell. The mean average precision of the proposed custom model is 86.5%mAP, making it suitable for the upcoming COVID-19 monitoring systems. https://journal.mtu.edu.iq/index.php/MTU/article/view/1174YoloConvolutional Neural NetworkCoronavirusObject Detection |
| spellingShingle | Noor Najah Ali Aseel Hameed Asanka G. Perera Ali Al_Naji Custom YOLO Object Detection Model for COVID-19 Diagnosis Journal of Techniques Yolo Convolutional Neural Network Coronavirus Object Detection |
| title | Custom YOLO Object Detection Model for COVID-19 Diagnosis |
| title_full | Custom YOLO Object Detection Model for COVID-19 Diagnosis |
| title_fullStr | Custom YOLO Object Detection Model for COVID-19 Diagnosis |
| title_full_unstemmed | Custom YOLO Object Detection Model for COVID-19 Diagnosis |
| title_short | Custom YOLO Object Detection Model for COVID-19 Diagnosis |
| title_sort | custom yolo object detection model for covid 19 diagnosis |
| topic | Yolo Convolutional Neural Network Coronavirus Object Detection |
| url | https://journal.mtu.edu.iq/index.php/MTU/article/view/1174 |
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