A Novel Computer-Aided Approach for Predicting COVID-19 Severity Using Hyperparameters in ResNet50v2 from X-ray Images
Journal Title: International Journal of Experimental Research and Review - Year 2024, Vol 42, Issue 6
Abstract
This research has been globally impacted by COVID-19 virus, which was a very uncommon, highly contagious & dangerous respiratory illness demanding early detection for effective containment and further spread. In this research, we proposed an innovative methodology that utilizes images of X-rays for COVID-19 detection at an early stage. By employing a convolution neural network, we enhance the accuracy performance via using ResNet50v2 using a hyperparameter. The methodology achieves a remarkable accuracy with an average accuracy of 99.12%. This accuracy surpasses other available models based on different deep learning models like VGG, Xception and DenseNet for COVID identification & detection with the help of X-ray images. X-ray scans are now preferably used modality for the identification & detection of COVID-19, given its widespread utilization and effectiveness. However, manual treatment & examination using X-ray images is very challenging, specifically in the field which is facing a limitation of skilled medical staff. Utilization of deep learning models has demonstrated significant potential and effective results in automating the diagnosis for timely identification of COVID with the help of X-ray films. The suggested architecture is specifically developed for timely prediction and analysis of COVID cases employing X-ray films. It firmly believes that this study holds significant potential in alleviating the workload of frontline radiologists, expediting patient diagnosis and treatment, and facilitating pandemic control efforts.
Authors and Affiliations
Rahul Deva, Arvind Dagur
Keywords
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- EP ID EP743740
- DOI 10.52756/ijerr.2024.v42.011
- Views 39
- Downloads 0
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