COVID-19 Severity Classification on Chest X-ray Images

• URL: http://arxiv.org/abs/2205.12705v1
• Date: Wed, 25 May 2022 12:01:03 GMT
• Title: COVID-19 Severity Classification on Chest X-ray Images
• Authors: Aditi Sagar, Aman Swaraj, Karan Verma
• Abstract summary: In this work, we classify covid images based on the severity of the infection. 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)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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