Deep Learning for COVID-19 Diagnosis Using Pretrained and Non-Pretrained Models

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ID: 313107
2024
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Abstract
This article proposes a deep-learning approach to classify COVID-19 cases using image data. Our model uses a convolutional neural network (CNN) to extract features from chest X-rays and classify them as positive or negative for COVID-19. A COVID-19 case dataset is compared to traditional machine learning methods to evaluate model performance. The results obtained demonstrate the effectiveness of the deep learning model in accurately detecting COVID-19 cases with an overall accuracy of 96%. This approach is helpful for rapid and automated diagnosis of COVID-19, especially in resource-limited settings. The proposed method yielded remarkable results compared with recent results.
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Authors Muhammad Basit Umair, Muhammad Tufail, Muhammad Asgher Nadeem, Sajjad Ahmad, Durr Muhammad, Maria Khalid, Muhammad Azhar Mushtaq, Sadaqat Ali Ramay, Sayyid Kamran Hussain
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
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URL
https://jcbi.org/index.php/Main/article/view/395
Keywords Keywords not found

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