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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).


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.



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,", Image and Video Processing, arXiv:2004.03747, pp. 1-11, April 2020.


Vision Lab, Dr. Vijayan Asari, Director

Kettering Lab
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Dayton, Ohio 45469 - 0232