Feature selection using regression mutual information deep convolution neuron networks for COVID-19 X-ray image classification

Chest radiography (CXR) image is usually required for lung severity assessment. However, chest X-rays in COVID-19 interpretation is required expert radiologists’ knowledge. This study aims to improve the COVID-19 X-ray image classification using feature selection technique by the regression mutual information deep convolution neuron networks (RMI Deep-CNNs). The dataset consists of 219 COVID-19, 500 viral pneumonias, and 500 normal chest X-ray images. CXR images were comprehensively pre-trained using DCNNs to extract the very large image features, then, the feature selection could reduce the complexity of a model and reduce the model overfitting. Therefore, the critical features were selected using regression mutual information followed by the fully connected with softmax layer for classification. For the classification of two alternative systems, these networks were compared (ResNet152V2 and InceptionV3). The classification performance for both schemes were 92.21%, 100%, 90% and 91.39%, 100%, 82.50%, respectively. In addition, RMI Deep-CNNs not only improve the accuracy but also reduce trainable features by over 80%. This approach tends to significantly improve the computation time and model accuracy for COVID‐19 classification.