  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.
