MiniCOVIDNET: Lung Ultrasound based Point of Care Detection of COVID19


REFERENCE: 
Navchetan Awasthi, Aveen Dayal, Linga R. Cenkeramaddi, and Phaneendra K. Yalavarthy, "Mini-COVIDNet : Efficient Light Weight Deep Neural Network for Ultrasound based Point-of-Care Detection of COVID-19," IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 68(6), 2023-2037 (2021). [doi: 10.1109/TUFFC.2021.3068190]. Coverpage article
Cover

Project Code Repository: https://github.com/navchetan-awasthi/Mini-COVIDNet

Acknowledgements:
    - Indian Institute of Science, Bangalore
    - University of Agder, Norway
This work in part was supported by the WIPRO-GE Collaborative Laboratory on Artificial Intelligence in Healthcare and Medical  Imaging and Indo-Norwegian collaboration in Autonomous Cyber-Physical Systems (INCAPS) project: 287918 of International Partnerships for Excellent Education, Research and Innovation (INTPART) program from the Research Council of Norway.

Disclaimer:
The software and applications developed are not intended, nor should they be construed, as claims that this can be used to diagnose, treat, mitigate, cure, prevent or otherwise be used for any disease or medical condition. The software/application has not been clinically proven or evaluated.

Media Coverage





AnamNET: Chest Computed Tomography based COVID-19 Scoring


ANAMnet Key Steps

Key steps in the proposed approach for automated segmentation of abnormalities/anomalies in chest CT images (Credit: IEEE)


Covseg Android Application:
Covseg

REFERENCE: 
Paluru N, Dayal A, Jenssen HB, Sakinis T, Cenkeramaddi LR, Prakash J, Yalavarthy PK. Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images. IEEE Trans Neural Netw Learn Syst. (Fast Track: COVID-19 Focused Papers) 32(3), 932-946 (2021). [doi: 10.1109/TNNLS.2021.3054746]

 https://ieeexplore.ieee.org/document/9349153/


Acknowledgements:
    - Indian Institute of Science, Bangalore
    - University of Agder, Norway
    - Oslo University Hospital, Norway
This work in part was supported by the WIPRO GE-CDS Collaborative Laboratory on Artificial Intelligence in Healthcare and Medical Imaging as well as Indo-Norwegian Collaboration in Autonomous Cyber-Physical Systems (INCAPS), INTPART Programme, Research Council of Norway.

Disclaimer:
The software and applications developed are not intended, nor should they be construed, as claims that this can be used to diagnose, treat, mitigate, cure, prevent or otherwise be used for any disease or medical condition. The software/application has not been clinically proven or evaluated.


Media Coverage:





















 

COVID-19 RESEARCH