Prediction of COVID-19-Pneumonia based on Selected Deep Features and One Class Kernel Extreme Learning Machine

In this work, we propose a deep learning framework for the classification of COVID-19 pneumonia infection from normal chest CT scans. In this regard, a 15-layered convolutional neural network architecture is developed which extracts deep features from the selected image samples ? collected from the Radiopeadia. Deep features are collected from two different layers, global average pool and fully connected layers, which are later combined using the max-layer detail (MLD) approach. Subsequently, a Correntropy technique is embedded in the main design to select the most discriminant features from the pool of features. One-class kernel extreme learning machine classifier is utilized for the final classification to achieving an average accuracy of 95.1%, and the sensitivity, specificity & precision rate of 95.1%, 95%, & 94% respectively. To further verify our claims, detailed statistical analyses based on standard error mean (SEM) is also provided, which proves the effectiveness of our proposed prediction design.

Automated summary

The study introduces a deep learning framework for classifying COVID-19 pneumonia infection from normal chest CT scans, achieving high accuracy through the development of a convolutional neural network architecture combined with feature extraction, selection, and classification techniques. A one-class kernel extreme learning machine classifier is utilized for final classification with an average accuracy of 95.1%.