Many health systems over the world have collapsed due to limited capacity and a
dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an
efficient, quick and accurate method to mitigate the overloading of radiologists’ efforts to diagnose
the suspected cases. This study presents the combination of deep learning of extracted features with
the Q-deformed entropy handcrafted features for discriminating between COVID-19 coronavirus,
pneumonia and healthy computed tomography (CT) lung scans. In this study, pre-processing is
used to reduce the effect of intensity variations between CT slices. Then histogram thresholding is
used to isolate the background of the CT lung scan. Each CT lung scan undergoes a feature
extraction which involves deep learning and a Q-deformed entropy algorithm. The obtained
features are classified using a long short-term memory (LSTM) neural network classifier.
Subsequently, combining all extracted features significantly improves the performance of the LSTM
network to precisely discriminate between COVID-19, pneumonia and healthy cases. The maximum
achieved accuracy for classifying the collected dataset comprising 321 patients is 99.68%.
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