COVID-19 detection from CT scans using a two-stage framework

作者：

Highlights：

• An end-to-end framework is developed to detect COVID-19 from CT scan images.

• 3 standard CNN models (DenseNet, ResNet, Xception) are used as feature extractors.

• Feature selection is done by Harmony Search and Adaptive β-Hill Climbing.

• The proposed framework is evaluated on the SARS-COV-2 CT-Scan Dataset.

摘要

•An end-to-end framework is developed to detect COVID-19 from CT scan images.•3 standard CNN models (DenseNet, ResNet, Xception) are used as feature extractors.•Feature selection is done by Harmony Search and Adaptive β-Hill Climbing.•The proposed framework is evaluated on the SARS-COV-2 CT-Scan Dataset.

论文关键词：COVID-19 detection,Convolutional Neural Network,Harmony Search,Adaptive β-Hill Climbing

论文评审过程：Received 16 March 2021, Revised 9 November 2021, Accepted 4 December 2021, Available online 1 January 2022, Version of Record 5 January 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116377