Biased Deep Learning Methods in Detection of COVID-19 Using CT Images: A Challenge Mounted by Subject-Wise-Split ISFCT Dataset

Biased Deep Learning Methods in Detection of COVID-19 Using CT Images: A Challenge Mounted by Subject-Wise-Split ISFCT Dataset

Accurate detection of respiratory system damage including COVID-19 is considered one of the crucial applications of deep learning (DL) models using CT images. However, the main shortcoming of the published works has been unreliable reported accuracy and the lack of repeatability with new datasets, m...

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Bibliographic Details
Main Authors: Shiva Parsarad, Narges Saeedizadeh, Ghazaleh Jamalipour Soufi, Shamim Shafieyoon, Farzaneh Hekmatnia, Andrew Parviz Zarei, Samira Soleimany, Amir Yousefi, Hengameh Nazari, Pegah Torabi, Abbas S. Milani, Seyed Ali Madani Tonekaboni, Hossein Rabbani, Ali Hekmatnia, Rahele Kafieh
Format: Article
Language:English
Published: MDPI AG 2023-08-01
Series:Journal of Imaging
Subjects:
COVID-19
deep learning
subject-wise data split
slice-wise data split
repeatability
Online Access:https://www.mdpi.com/2313-433X/9/8/159

Internet

https://www.mdpi.com/2313-433X/9/8/159

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