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This paper implemented DL approaches using a One Dimensional-Convolutional Neural Network (1D-CNN) followed by an RNN classifier with and without Bayesian hyperparameter optimization (BO). The accuracy of this hybrid model combination of (BO\u2009+\u20091D-CNN\u2009+\u2009RNN) produced the highest classification accuracy of 100% instead of the 95% for the ML model in prior work and 90% for the (1D-CNN\u2009+\u2009RNN) algorithm considered in the paper. Therefore, the classification of brain cancer gene expression according to the hybrid model (BO\u2009+\u20091D-CNN\u2009+\u2009RNN) provides more accurate and useful assessments for patients with different types of brain cancers. Thus, gene expression data are used to create a DL classification-based- hybrid model that will hold senior promise in the treatment of brain cancer.<\/jats:p>","DOI":"10.1007\/s40747-024-01555-4","type":"journal-article","created":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T09:02:45Z","timestamp":1721811765000},"page":"7605-7617","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Leveraging hybrid 1D-CNN and RNN approach for classification of brain cancer gene expression"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6279-0883","authenticated-orcid":false,"given":"Heba M.","family":"Afify","sequence":"first","affiliation":[]},{"given":"Kamel K.","family":"Mohammed","sequence":"additional","affiliation":[]},{"given":"Aboul Ella","family":"Hassanien","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,24]]},"reference":[{"key":"1555_CR1","first-page":"838","volume":"22","author":"Z Tao","year":"2017","unstructured":"Tao Z, Shi A, Li R et al (2017) Microarray bioinformatics in cancer\u2014A review. 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