1. Modeling EfficientNet-B3 model for AI-based COVID-19 detection in chest x-rays.
- Author
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Tripathi, Abhay, Alkhayyat, Ahmed, Bhatt, Arvind Kumar, Sharma, Moolchand, and Sheikh, Tariq Hussain
- Subjects
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X-rays , *COMPUTER-aided diagnosis , *ARTIFICIAL intelligence , *X-ray detection , *DEEP learning , *COVID-19 , *COVID-19 pandemic - Abstract
Novel Corona-virus (COVID-19) must be recognized immediately and precisely to avoid or contain a possible pandemic by immediate quarantine and appropriate medical treatment. Detecting a disease will be challenging due to the increased number of COVID-19 patients and the virus' mutation. However, computer-assisted medical diagnosis has attained cutting-edge outcomes using artificial intelligence and deep learning methodologies. This study uses current resources and powerful deep learning methods to present an alternative diagnostic tool for COVID-19 instances. This work aims to investigate the feasibility of combining state-of-the-art classifiers for the most accurate detection of COVID-19 from chest X-ray images. The model utilizes the IEEE covid chest X-ray dataset (https://github.com/ieee8023/covid-chestxray-dataset) and the EfficientNet architecture to predict an accuracy of 99 percent. As a result, a model such as this one can assist medical practitioners in diagnosing COVID-19 cases more quickly than a radiologist reading through each scan one by one, mainly when many patients must be evaluated in short period. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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