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COVID-19 Severity Classification on Chest X-ray Images

arXiv 2205.12705 ↗First posted 25 May 2022eess.IV cs.CVcited by 0

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Abstract (arXiv)

Biomedical imaging analysis combined with artificial intelligence (AI) methods has proven to be quite valuable in order to diagnose COVID-19. So far, various classification models have been used for diagnosing COVID-19. However, classification of patients based on their severity level is not yet analyzed. In this work, we classify covid images based on the severity of the infection. First, we pre-process the X-ray images using a median filter and histogram equalization. Enhanced X-ray images are then augmented using SMOTE technique for achieving a balanced dataset. Pre-trained Resnet50, VGG16 model and SVM classifier are then used for feature extraction and classification. The result of the classification model confirms that compared with the alternatives, with chest X-Ray images, the ResNet-50 model produced remarkable classification results in terms of accuracy (95%), recall (0.94), and F1-Score (0.92), and precision (0.91).

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References · 13

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  11. 2020Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Imagescited by 8
  12. 2021Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID-19 Casescited by 5
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  1. 2021Classification of COVID-19 on chest X-Ray images using Deep Learning model with Histogram Equalization and Lungs Segmentationcited by 3

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