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Alessandro de Oliveira Faria
State of São Paulo
Treatise of Medical Image Processing (TMIP) v0.2.0
Experiment 2: Coronavirus (2019-nCoV infection) Recognition using Deep Neural Networks for Computer Tomography (CT) image analysis. ...learn more
Project status: Published/In Market
Internet of Things, Artificial Intelligence, Cloud
Intel Technologies
DevCloud,
oneAPI,
Intel Opt ML/DL Framework,
MKL,
OpenVINO
Overview / Usage
On Dec. 31, 2019, the World Health Organization (WHO) learned of several cases of a respiratory illness clinically resembling viral pneumonia and manifesting as fever, cough, and shortness of breath. The newly discovered virus emerging from Wuhan City, Hubei Province of China, was temporarily named “novel coronavirus” (2019-nCoV). It is now known officially as COVID-19. This new coronavirus belongs to a family of viruses that include Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS).
The outbreak is escalating quickly, with hundreds of thousands of confirmed COVID-19 cases reported globally. Early disease recognition is critical not only for prompt treatment, but also for patient isolation and effective public health containment and response. Thus we propose the use of AI based CT image analysis for recognition of coronavirus infection.
Methodology / Approach
Coronavirus (2019-nCoV infection) recognition using Deep Neural Networks for Computer Tomography (CT) image analysis . Next steps is to work on webapp using webRTC for doctors around the world
Technologies Used
Intel Inside: Intel® Xeon E5-2690 v3 , Intel® MKL-DNN, Intel® Optimized TensorFlow, Intel® Distribution of OpenVINO™ toolkit, Intel® Open Visual Cloud, Intel® DevCloud
Repository
https://github.com/TebogoNakampe/TMIP-2019-nCoV-Recognition.git
Other links
Collaborators