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First, it generates a set of models based on the transfer learning strategy from deep neural networks. Then, the relevant subset of models is selected by the particle swarm optimization algorithm and combined by voting or averaging methods. The proposed approach was tested on a histopathological dataset for colorectal cancer classification, based on seven types of CNNs. The method has achieved accurate results (94.52%) by the Resnet121 model and the voting strategy, which provides important insights into the efficiency of dynamic ensembling in deep learning.<\/p>","DOI":"10.4018\/ijsir.2020070105","type":"journal-article","created":{"date-parts":[[2020,5,19]],"date-time":"2020-05-19T15:50:58Z","timestamp":1589903458000},"page":"72-88","source":"Crossref","is-referenced-by-count":24,"title":["A New Deep Learning Model Selection Method for Colorectal Cancer Classification"],"prefix":"10.4018","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8683-3163","authenticated-orcid":true,"given":"Nassima","family":"Dif","sequence":"first","affiliation":[{"name":"EEDIS Laboraory, Djillali Liabes University, Sidi Bel Abbes, Algeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3391-6280","authenticated-orcid":true,"given":"Zakaria","family":"Elberrichi","sequence":"additional","affiliation":[{"name":"EEDIS Laboraory, Djillali Liabes University, Sidi Bel Abbes, Algeria"}]}],"member":"2432","reference":[{"key":"IJSIR.2020070105-0","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0177544"},{"key":"IJSIR.2020070105-1","doi-asserted-by":"publisher","DOI":"10.3389\/fnana.2015.00142"},{"key":"IJSIR.2020070105-2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2476509"},{"key":"IJSIR.2020070105-3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-23937-4_7"},{"key":"IJSIR.2020070105-4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59480-4_3"},{"key":"IJSIR.2020070105-5","doi-asserted-by":"crossref","unstructured":"Chen, H., Dou, Q., Wang, X., Qin, J., & Heng, P. 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