  This paper extends our previous method for COVID-19 diagnosis, proposing an
enhanced solution for detecting COVID-19 from computed tomography (CT) images.
To decrease model misclassifications, two key steps of image processing were
employed. Firstly, the uppermost and lowermost slices were removed, preserving
sixty percent of each patient's slices. Secondly, all slices underwent manual
cropping to emphasize the lung areas. Subsequently, resized CT scans (224 by
224) were input into an Xception transfer learning model. Leveraging Xception's
architecture and pre-trained weights, the modified model achieved binary
classification. Promising results on the COV19-CT database showcased higher
validation accuracy and macro F1 score at both the slice and patient levels
compared to our previous solution and alternatives on the same dataset.
