LEVERAGING RESNET-152 AND WEB TECHNOLOGY FOR RAPID COVID-19 DIAGNOSIS FROM X-RAY IMAGE

JENNY NIE, LING SIAW and Chai, Soo See and Goh, Kok Luong and Chin, Kim On (2024) LEVERAGING RESNET-152 AND WEB TECHNOLOGY FOR RAPID COVID-19 DIAGNOSIS FROM X-RAY IMAGE. Journal of Theoretical and Applied Information Technology, 102 (23). pp. 8577-8590. ISSN 1817-3195

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Abstract

In December 2019, the SARS-CoV-2 virus gave rise to COVID-19, which was first detected in Wuhan, China. The virus has infected over 700 million individuals on Earth. This virus can spread through direct and indirect contact, making humans vulnerable even in small places or through food consumption. The pandemic highlighted challenges, including a shortage of radiologists and the time-intensive interpretation of X-ray images, leading to discrepancies and delays. To address this, a classification model based on X-ray images became crucial for COVID-19 identification. Proposing a web-based system integrating convolutional neural network (CNN) models, particularly the ResNet-152 model, aims to enhance precision in monitoring and diagnosing COVID-19. After fine-tuning a pre-trained ResNet-152 model using transfer learning on a COVID-19 dataset and adding a classification head, a COVID-19-specific classification model is created. In this project, the pre-trained COVID-19 ResNet-152 model achieved 86.84% accuracy, 89.95% sensitivity and 77.27% specificity. The model is then integrated into the system, which enables healthcare professionals to upload and receive a clear visualisation of the COVID-19 classification results via Application Programming Interface (API) endpoints. This platform enables healthcare professionals to login, upload, search, and classify COVID-19 diagnoses based on the uploaded X-ray pictures, providing an intuitive interface and a user-friendly system. Leveraging advanced image processing and deep learning, the system has the potential to expedite accurate diagnoses and alleviate the workload on healthcare professionals, ensuring swift and accurate detection of COVID-19 cases.

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