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%.

In the present paper, we consider the generalized Hyers–Ulam stability for fractional differential equations of the form: [Formula: see text] in a complex Banach space. Furthermore, applications are illustrated.

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In this paper, we investigate the existence and uniqueness of solutions for the following class of multiorder fractional differential equationswhere D γ i ,δ i β i ,n denotes the generalized Erdélyi-Kober operator of fractional derivative of order δ i . Moreover, some properties concerning the positive, maximal, minimal, and continuation of solutions are obtained.