Mohd Arfian, Ismail and Siti Nur Fathin Najwa, Mustaffa and Abed, Munther H. (2023) AI-Enabled Deep Learning Model for COVID-19 Identification Leveraging Internet of Things. Wasit Journal of Computer and Mathematics Science, 2 (2). pp. 33-39. ISSN 2788-5879. (Published)
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Abstract
Addressing the COVID-19 epidemic since December 2019 has emphasized the criticality of timely and error-free identification of infected COVID-19 individuals in medical settings. To effectively combat the epidemic, the utilization of deep TL-enabled automated COVID-19 identification on CXRs is paramount. This study recommended a real-time IoT system employing ensemble deep TL to enable early identification of infected COVID-19 individuals. The system allows for real-time transmission and identification of COVID-19 suspicious individuals. The suggested IoT model incorporates several DL models, including InceptionResNetV2, VGG16, ResNet152V2, and DenseNet201. These models, stored on a cloud server, are utilized in conjunction with medical sensors to gather chest X-ray data and detect infections. A chest X-ray dataset is used to compare the deep ensemble model against six transfer learning algorithms. The comparative investigation demonstrates that the suggested approach facilitates swift and effective diagnosis of COVID-19 suspicious patients, providing valuable support to radiologists. This work highlights the significance of leveraging deep transfer learning and IoT in achieving early identification of suspected COVID-19 patients. The proposed system, incorporating a deep ensemble model, offers a practical solution for assisting radiologists in efficiently diagnosing COVID-19 cases
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | IoT, COVID-19, deep transfer learning, medical treatment and Artificial Intelligent |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Computing |
| Depositing User: | Miss Amelia Binti Hasan |
| Date Deposited: | 17 Oct 2023 03:47 |
| Last Modified: | 17 Oct 2023 03:47 |
| URI: | http://umpir.ump.edu.my/id/eprint/38906 |
| Download Statistic: | View Download Statistics |
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