Complex features extraction with deep learning model for the detection of COVID19 from CT scan images using ensemble based machine learning approach

作者：

Highlights：

• Achieved an accuracy of 99.73% and a recall of 100%.

• Enhance the quality of the CT scan images using CLAHE.

• Build a novel CNN for extracting the most relevant features from the CT scan images.

• Develop a soft voting ensemble learning model for improving the performance.

摘要

•Achieved an accuracy of 99.73% and a recall of 100%.•Enhance the quality of the CT scan images using CLAHE.•Build a novel CNN for extracting the most relevant features from the CT scan images.•Develop a soft voting ensemble learning model for improving the performance.

论文关键词：CLAHE,Convolutional Neural Network,COVID19,Ensemble learning,Feature scaling,Guassian Naive Bayes,Support Vector Machine,Soft voting

论文评审过程：Received 29 March 2021, Revised 5 January 2022, Accepted 14 January 2022, Available online 4 February 2022, Version of Record 7 February 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116554