Advancing COVID-19 differentiation with a robust preprocessing and integration of multi-institutional open-repository computer tomography datasets for deep learning analysis

• Published in: Experimental and therapeutic medicine 2020-11, Vol.20 (5), p.78-1
• Main Authors: Trivizakis, Eleftherios, Tsiknakis, Nikos, Vassalou, Evangelia E, Papadakis, Georgios Z, Spandidos, Demetrios A, Sarigiannis, Dimosthenis, Tsatsakis, Aristidis, Papanikolaou, Nikolaos, Karantanas, Apostolos H, Marias, Kostas
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Automation; Cable television broadcasting industry; China; Coronaviruses; COVID-19; Datasets; Deep learning; Disease transmission; Epidemics; Greece; Image databases; Immune system; Information management; Medical imaging; Medical imaging equipment; Pandemics; Patients; Pneumonia; Tomography; United States
• ISSN: 1792-0981; 1792-1015
• DOI: 10.3892/etm.2020.9210

The coronavirus pandemic and its unprecedented consequences globally has spurred the interest of the artificial intelligence research community. A plethora of published studies have investigated the role of imaging such as chest X-rays and computer tomography in coronavirus disease 2019 (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.