Discovery of a Generalization Gap of Convolutional Neural Networks on COVID-19 X-Rays Classification

Author:

Ahmed Kaoutar BenORCID,Goldgof Gregory M.ORCID,Paul RahulORCID,Goldgof Dmitry B.ORCID,Hall Lawrence O.ORCID

Funder

NSF

NIH

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Cited by 25 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Joint classification and segmentation for an interpretable diagnosis of acute respiratory distress syndrome from chest x-rays;Journal of Medical Imaging;2023-10-17

2. Analysis of generalizability on predicting COVID-19 from chest X-ray images using pre-trained deep models;Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization;2023-10-11

3. Transparency in Medicine: How eXplainable AI is Revolutionizing Patient Care;2023 International Conference on Network, Multimedia and Information Technology (NMITCON);2023-09-01

4. Deep learning for understanding multilabel imbalanced Chest X-ray datasets;Future Generation Computer Systems;2023-07

5. A convolutional neural network with self-attention for fully automated metabolic tumor volume delineation of head and neck cancer in $$[^{18}$$F]FDG PET/CT;European Journal of Nuclear Medicine and Molecular Imaging;2023-04-20

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