Automatic Deep Learning System for COVID-19 Infection Quantification in chest CT

• URL: http://arxiv.org/abs/2010.01982v1
• Date: Thu, 1 Oct 2020 21:05:59 GMT
• Title: Automatic Deep Learning System for COVID-19 Infection Quantification in chest CT
• Authors: Omar Ibrahim Alirr
• Abstract summary: This paper proposes an automatic deep learning system for COVID-19 infection areas segmentation. The proposed FCN is implemented using U-net architecture with modified residual block with concatenation skip connection. To demonstrate the generalization and effectiveness of the proposed system, it is trained and tested using many 2D CT slices extracted from diverse datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Coronavirus Disease spread globally and infected millions of people quickly, causing high pressure on the health-system facilities. PCR screening is the adopted diagnostic testing method for COVID-19 detection. However, PCR is criticized due its low sensitivity ratios, also, it is time-consuming and manual complicated process. CT imaging proved its ability to detect the disease even for asymptotic patients, which make it a trustworthy alternative for PCR. In addition, the appearance of COVID-19 infections in CT slices, offers high potential to support in disease evolution monitoring using automated infection segmentation methods. However, COVID-19 infection areas include high variations in term of size, shape, contrast and intensity homogeneity, which impose a big challenge on segmentation process. To address these challenges, this paper proposed an automatic deep learning system for COVID-19 infection areas segmentation. The system include different steps to enhance and improve infection areas appearance in the CT slices so they can be learned efficiently using the deep network. The system start prepare the region of interest by segmenting the lung organ, which then undergo edge enhancing diffusion filtering (EED) to improve the infection areas contrast and intensity homogeneity. The proposed FCN is implemented using U-net architecture with modified residual block with concatenation skip connection. The block improves the learning of gradient values by forwarding the infection area features through the network. To demonstrate the generalization and effectiveness of the proposed system, it is trained and tested using many 2D CT slices extracted from diverse datasets from different sources. The proposed system is evaluated using different measures and achieved dice overlapping score of 0.961 and 0.780 for lung and infection areas segmentation, respectively.

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