Advancing Covidā€‘19 differentiation with a robust preprocessing and integration of multiā€‘institutional openā€‘repository computer tomography datasets for deep learning analysis


I am pleased to announce that our second research article on COVID-19, Advancing Covidā€‘19 differentiation with a robust preprocessing and integration of multiā€‘institutional openā€‘repository computer tomography datasets for deep learning analysis, has been published.

In this paper, we developed a Deep Learning pipeline for COVID-19 screening from CT images. Firstly, a stateā€‘ofā€‘theā€‘art custom Uā€‘Net model is applied to the CT image, in order to properly segment the lung areas. The segmentation model achieves a DICE similarity coefficient performance of 99.6%. Secondly, a classification network, which is based on a pre-trained VGG-19 model, is applied on the segmented images for COVIDā€‘19 versus pneumonia differentiation and exhibits an area under curve (AUC) of 96.1%.

If you find it interesting, please cite it:

Trivizakis, E., Tsiknakis, N., Vassalou, E.E., Papadakis, G.Z., Spandidos, D.A., Sarigiannis, D., Tsatsakis, A., Papanikolaou, N., Karantanas, A.H. and Marias, K., 2020. Advancing COVIDā€‘19 differentiation with a robust preprocessing and integration of multiā€‘institutional openā€‘repository computer tomography datasets for deep learning analysis. Experimental and Therapeutic Medicine. https://doi.org/10.3892/etm.2020.9210

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