CovidConvLSTM: A fuzzy ensemble model for COVID-19 detection from chest X-rays

作者：

Highlights：

• Proposed a model to classify COVID-19, Pneumonia, and Normal chest X-ray images.

• Generated feature maps using three pre-trained standard deep learning models.

• Improved the spatial dependency among feature maps prior to use as features.

• Used squeeze-and-excitation block as a spatial attention scheme.

• Utilized the Sugeno fuzzy integral based classifier ensembling technique.

摘要

•Proposed a model to classify COVID-19, Pneumonia, and Normal chest X-ray images.•Generated feature maps using three pre-trained standard deep learning models.•Improved the spatial dependency among feature maps prior to use as features.•Used squeeze-and-excitation block as a spatial attention scheme.•Utilized the Sugeno fuzzy integral based classifier ensembling technique.

论文关键词：Sugeno integral,COVID-19,Deep learning,ConvLSTM,Pneumonia,Fuzzy ensemble

论文评审过程：Received 3 February 2021, Revised 5 June 2022, Accepted 6 June 2022, Available online 16 June 2022, Version of Record 25 June 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117812