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

COVID-19 Detection Using Segmentation, Region Extraction and Classification Pipeline