Deep reproductive feature generation framework for the diagnosis of COVID-19 and viral pneumonia using chest X-ray images

TL;DR

This paper introduces a two-stage deep learning framework combining pre-trained CNNs and autoencoders to accurately classify COVID-19, viral pneumonia, and normal cases from chest X-ray images, demonstrating superior performance and task-independence.

Contribution

The study proposes a novel, task-independent reproductive feature generation framework using CNNs and autoencoders for medical image classification, outperforming existing methods.

Findings

Outperforms other frameworks in binary classification.

Shows competitive results in three-class classification.

Features are more discriminative when task-independent.

Abstract

The rapid and accurate detection of COVID-19 cases is critical for timely treatment and preventing the spread of the disease. In this study, a two-stage feature extraction framework using eight state-of-the-art pre-trained deep Convolutional Neural Networks (CNNs) and an autoencoder is proposed to determine the health conditions of patients (COVID-19, Normal, Viral Pneumonia) based on chest X-rays. The X-ray scans are divided into four equally sized sections and analyzed by deep pre-trained CNNs. Subsequently, an autoencoder with three hidden layers is trained to extract reproductive features from the concatenated ouput of CNNs. To evaluate the performance of the proposed framework, three different classifiers, which are single-layer perceptron (SLP), multi-layer perceptron (MLP), and support vector machine (SVM) are used. Furthermore, the deep CNN architectures are used to create benchmark models and trained on the same dataset for comparision. The proposed framework outperforms other frameworks wih pre-trained feature extractors in binary classification and shows competitive results in three-class classification. The proposed methodology is task-independent and suitable for addressing various problems. The results show that the discriminative features are a subset of the reproductive features, suggesting that extracting task-independent features is superior to the extraction only task-based features. The flexibility and task-independence of the reproductive features make the conceptive information approach more favorable. The proposed methodology is novel and shows promising results for analyzing medical image data.