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To improve the performance of the Lung CAD system, algorithmic research is carried out for the above two parts, respectively. First, in view of the poor interpretability of deep features and the incomplete expression of clinically defined handcrafted features, a feature cascade method is proposed to obtain richer feature information of nodules as the final input of the classifier. Second, to better map the global characteristics of samples, the multiple kernel learning support vector machine (MKL\u2010SVM) algorithm with a linear convex combination of polynomial kernel and sigmoid kernel is proposed. Furthermore, this paper applied the methods for speed contraction factor and roulette strategy, and a mixture of simulated annealing (SA) and particle swarm optimization (PSO) is used for global optimization, so as to solve the problem that the PSO is easy to lose particle diversity and fall into the local optimal solution as well as improve the model\u2019s training speed. Therefore, the MKL\u2010SVM algorithm is presented in this paper, which is based on swarm intelligence optimization is proposed for lung nodule recognition. Finally, the algorithm construction experiments are conducted on the cooperative hospital dataset and compared with 8 advanced algorithms on the public dataset LUNA16. The experimental results show that the proposed algorithms can improve the accuracy of lung nodule recognition and reduce the missed detection of nodules.<\/jats:p>","DOI":"10.1155\/2021\/5491017","type":"journal-article","created":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T23:26:22Z","timestamp":1630970782000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Research on Key Algorithms of the Lung CAD System Based on Cascade Feature and Hybrid Swarm Intelligence Optimization for MKL\u2010SVM"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8512-1837","authenticated-orcid":false,"given":"Jiayue","family":"Chang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5543-8171","authenticated-orcid":false,"given":"Yang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2573-4556","authenticated-orcid":false,"given":"Hewei","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,9,6]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21654"},{"key":"e_1_2_9_2_2","article-title":"Global cancer statistics 2018: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"70","author":"Freddie B.","year":"2018","journal-title":"CA: A Cancer Journal for Clinicians"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1097\/cm9.0000000000001474"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-016-0669-0"},{"key":"e_1_2_9_5_2","unstructured":"SimonyanK.andZissermanA. 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