A Robust and Efficient Hybrid Classification Model for Early Diagnosis of Chest X-Ray Images of COVID-19
DOI:
https://doi.org/10.21123/bsj.2024.10494Keywords:
Chest X-Ray, Deep Learning, Machine Learning, Densenet201, Multilayer Perceptron AlgorithmAbstract
There has been a COVID-19 pandemic since December 2019, and successful medical treatment for COVID-19 patients requires rapid and accurate diagnosis. Fighting the COVID-19 pandemic requires an automated system that uses deep transfer learning to diagnose the virus on chest X-ray (CXR). CXR are frequently utilized in healthcare because they offer the potential for rapid and accurate disease diagnosis. Automated computer-aided diagnosis (CAD) systems incorporate ML or deep learning to enhance efficiency and accuracy, hence reducing future problems. Numerous AI systems based on deep learning can be employed for diagnosis; among the most widely used is the CNN, which was first developed and has demonstrated encouraging accuracy in identifying COVID-19 confirmed patients using CXR pictures. Through using X-ray images, this work will design ML and deep learning to provide faster diagnostics for Covid-19 infection. As a result, the deep transfer learning technique uses an existing model first, then applies the needed data to it again. Where a Densenet201transfer learning model was utilized, which is one of a DL techniques, as feature extraction and its combination with multilayer perceptron algorithm; these technique were applied to a data set of a National Institute Health (NIH), where several performance measures were utilized, such as precision, precision, specificity and sensitivity, as an experiment proved the efficiency of the algorithm used in terms of accuracy by 98.82%. These outcomes are encouraging when compared to other DL models that were trained on the identical dataset.
Received 25/12/2023
Revised 28/06/2024
Accepted 30/06/2024
Published Online First 20/10/2024
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