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CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images

Medicine and Health

CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images

T. Javaheri, M. Homayounfar, et al.

Enhancing the accuracy of Covid-19 diagnosis is crucial, and the introduction of CovidCTNet could be a game-changer. This innovative open-source deep learning framework has achieved an impressive 95% accuracy using CT images, surpassing the radiologists' accuracy of 70%. Researchers like Tahereh Javaheri and Morteza Homayounfar have contributed to this significant advancement in the field.

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Playback language: English
Abstract
Early and accurate diagnosis of Covid-19 is crucial. Current methods like RT-PCR have limitations in accuracy. CT imaging offers higher sensitivity but lacks specificity. This paper introduces CovidCTNet, an open-source deep learning framework using a small cohort of CT images to improve Covid-19 diagnosis accuracy to 95%, surpassing radiologists' accuracy of 70%. CovidCTNet uses BCDU-Net for preprocessing and a CNN for classification, handling heterogeneous data and small sample sizes.
Publisher
npj Digital Medicine
Published On
Feb 18, 2021
Authors
Tahereh Javaheri, Morteza Homayounfar, Zohreh Amoozgar, Reza Reiazi, Fatemeh Homayounieh, Engy Abbas, Azadeh Laali, Amir Reza Radmard, Mohammad Hadi Gharib, Seyed Ali Javad Mousavi, Omid Ghaemi, Rosa Babaei, Hadi Karimi Mobin, Mehdi Hosseinzadeh, Rana Jahanban-Esfahlan, Khaled Seidi, Mannudeep K. Kalra, Guanglan Zhang, L. T. Chitkushev, Benjamin Haibe-Kains, Reza Malekzadeh, Reza Rawassizadeh
Tags
Covid-19
deep learning
CT imaging
diagnosis
CovidCTNet
accuracy
BCDUNet

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