COVID-19 Detection Using Segmentation, Region Extraction and Classification Pipeline
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Purpose The main purpose in this study is to propose 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 region of interest extraction part, and a classifier part. Methods The methodology used in the segmentation part is traditional segmentation methods as well as UNet based segmentation. In the classification part a Convolutional Neural Network (CNN) was used to take the final diagnosis decisions. Results In the segmentation part, the proposed segmentation methods show high dice scores on a publicly vailable dataset. In the classification part, the results show high accuracy on the validation partition of COV19-CT-DB dataset as well as higher precision, recall, and macro F1 score. The classification results were compared to our previous works other studies as well as on the same dataset. Conclusions The improved work in this paper proposes efficient pipeline with a potential of having clinical usage for COVID-19 detection and diagnosis via CT images. The code is on github at https://github.com/IDU-CVLab/COV19D_3rd
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