ABSTRACT
This paper proposes a deep neural networks model to predict COVID-19 patients automatically based on chest X-ray images. The model is trained using imbalance dataset with a new hybrid balancing technique proposed to solve this problem. The Deep Convolutional Neural VGG-16 is trained and utilized to extract features from a given chest X-ray image after some preprocessing steps. To overcome the data imbalance issue, a new hybrid Class Weights-SMOTE is applied to the extracted feature vector and compared with traditional balancing techniques. The feature vector is then classified utilizing a Fine-tuning VGG-16. The model provides a multi-classification for the input x-ray images into COVID-19, Normal, and Pneumonia. Comparison with existing methods shows that the proposed model achieves a superior classification accuracy and outperforms all other models, providing 98% accurate prediction and improving the model's performance on minority-class samples to achieve high accuracy 100%. The findings of this study could be useful for diagnosing COVID-19 from chest X-ray images. © 2022 Little Lion Scientific. All rights reserved.