HRDEL: High ranking deep ensemble learning-based lung cancer diagnosis model
作者：
Highlights：
• We proposed a novel intelligent method for lung cancer diagnosis.
• We proposed BF-SSA for mitigating the dimension of features.
• We develop HRDEL learning approach with a high-ranking process.
• Evaluation of the performance of the proposed approach for two different data sets.
• The efficiency of the proposed approach is better than existing methods.
摘要
•We proposed a novel intelligent method for lung cancer diagnosis.•We proposed BF-SSA for mitigating the dimension of features.•We develop HRDEL learning approach with a high-ranking process.•Evaluation of the performance of the proposed approach for two different data sets.•The efficiency of the proposed approach is better than existing methods.
论文关键词：Lung cancer diagnosis,High ranking deep ensemble learning,Feature extraction,Optimal feature selection,Convolutional neural network,Recurrent neural network
论文评审过程：Received 25 March 2022, Revised 18 September 2022, Accepted 30 September 2022, Available online 5 October 2022, Version of Record 11 October 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118956
