A three-stage ensemble boosted convolutional neural network for classification and analysis of COVID-19 chest x-ray images

Abstract

For the identification and classification of COVID-19, this research presents a three-stage ensemble boosted convolutional neural network model. A conventional segmentation model (ResUNet) is used to increase the model's performance in the initial step of processing the CXR datasets. In the second step, the CNN is used to extract the features from the pictures in the training dataset using machine learning techniques. Using machine learning (ML) techniques, the retrieved characteristics are then combined by voting in the third stage. There are 5178 aberrant CXR photos and 4310 normal CXR images used in this investigation. Models like CNN and ML can't compete with the suggested model. 99.35% of the model's measurements are accurate and precise, and 98% of its recall and F1-score are perfect. It is argued that the suggested model provides a rigorous and trustworthy evaluation of clinical decision-making in the setting of a public health crisis.

Keywords

• Three-stage ensemble boosted CNN
• CXR images
• COVID 19
• Machine Learning
• ResUnet