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COVID-19

COVID-19 Detection and Severity Assessment

Coronavirus Disease 2019 (COVID-19) is an infectious disease caused by a new virus. The disease causes respiratory illness with symptoms such as cough, fever, and in more severe cases, difficulty in breathing. The fast and accurate detection of the COVID-19 infection is essential to identify, make decisions and ensure treatment for the patients, which will help save their lives. We present a fast and efficient way to identify COVID-19 patients with multi-task deep learning (DL) methods. Both X-ray and CT scan images are considered to evaluate the proposed technique. We employ our Inception Residual Recurrent Convolutional Neural Network with Transfer Learning (TL) approach for COVID-19 detection and our NABLA-N network model for segmenting the regions infected by COVID-19.

The detection model shows around 84.67% testing accuracy from X-ray images and 98.78% accuracy in CT-images. We also present a novel quantitative analysis strategy to determine the percentage of infected regions in X-ray and CT images. The qualitative and quantitative results demonstrate promising results for COVID-19 detection and infected region localization.

An end-to-end system for COVID-19 detection with transfer learning from pneumonia detection. Bottom row in the figure shows the training phase which includes inputs, data augmentation, training model with TL and outputs (N: normal and C: COVID-19).

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The COVID-19 infected region detection results from lung CT images are shown below. Figure shows input images, segmented and refined masks with transfer learning approach, and infected region with heat maps.

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Research Publication

Md Zahangir Alom, M M Shaifur Rahman, Mst Shamima Nasrin, Tarek M. Taha, Vijayan K. Asari, "COVID_MTNet: COVID-19 detection with multi-task deep learning approaches," arXiv.org, Image and Video Processing, arXiv:2004.03747, pp. 1-11, April 2020. https://arxiv.org/abs/2004.03747

CONTACT

Vision Lab, Dr. Vijayan Asari, Director

Kettering Lab
300 College Park
Dayton, Ohio 45469 - 0232
937-229-1779
Email
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