  The main purpose of this study is to develop a pipeline for COVID-19
detection from a big and challenging database of Computed Tomography (CT)
images. The proposed pipeline includes a segmentation part, a lung extraction
part, and a classifier part. Optional slice removal techniques after UNet-based
segmentation of slices were also tried. The methodologies tried in the
segmentation part are traditional segmentation methods as well as UNet-based
methods. In the classification part, a Convolutional Neural Network (CNN) was
used to take the final diagnosis decisions. In terms of the results: in the
segmentation part, the proposed segmentation methods show high dice scores on a
publicly available dataset. In the classification part, the results were
compared at slice-level and at patient-level as well. At slice-level, methods
were compared and showed high validation accuracy indicating efficiency in
predicting 2D slices. At patient level, the proposed methods were also compared
in terms of validation accuracy and macro F1 score on the validation set. The
dataset used for classification is COV-19CT Database. The method proposed here
showed improvement from our precious results on the same dataset. In
Conclusion, the improved work in this paper has potential clinical usages for
COVID-19 detection and diagnosis via CT images. The code is on github at
https://github.com/IDU-CVLab/COV19D_3rd
