published:
Abstract. The coronavirus pandemic and its unprecedented consequences, globally has spurred the interest of the Artificial Intelligence research community. A plethora of published papers have investigated the role of imaging such as chest x-rays and computer tomography in COVID-19 automated diagnosis. Οpen repositories of medical imaging data can play a significant role by promoting cooperation among institutes in a world-wide scale. However, they may induce limitations related to variable data quality and intrinsic differences due to the wide variety of scanner vendors and imaging parameters. In this study, a state-of-the-art custom U-Net model is presented with a dice similarity coefficient performance of 99.6% along with a transfer learning VGG-19 based model for COVID-19 versus pneumonia differentiation exhibiting an area under curve of 96.1%. The above was significantly improved over the baseline model trained with no segmentation in selected tomographic slices of the same dataset. The presented study highlights the importance of a robust preprocessing protocol for image analysis within a heterogeneous imaging dataset and assesses the potential diagnostic value of the presented COVID-19 model by comparing its performance to the state of the art.
use hypes file with: https://github.com/trivizakis/easyConvNet
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please cite my work:
https://scholar.google.com/citations?user=CFsNV_4AAAAJ&hl=en&oi=ao