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Compared with existing work using small sample sizes, the GraphMriNet model has achieved classification accuracies of 100%, 100%, 100%, and 99.68% on the BMIBTD, CE-MRI, BTC-MRI, and FSB open datasets, respectively. The diagnostic accuracy has improved by 0.8% to 5.3% compared to existing research. In a few-shot scenario, GraphMriNet can accurately diagnose various types of brain tumors, providing crucial clinical guidance to assist doctors in making correct medical decisions. Additionally, the source code is available at this link: <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/keepgoingzhx\/GraphMriNet\">https:\/\/github.com\/keepgoingzhx\/GraphMriNet<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s40747-024-01530-z","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T05:02:23Z","timestamp":1719550943000},"page":"6917-6930","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["GraphMriNet: a few-shot brain tumor MRI image classification model based on Prewitt operator and graph isomorphic network"],"prefix":"10.1007","volume":"10","author":[{"given":"Bin","family":"Liao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8458-1089","authenticated-orcid":false,"given":"Hangxu","family":"Zuo","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"issue":"11","key":"1530_CR1","doi-asserted-by":"publisher","first-page":"4660","DOI":"10.1109\/TNNLS.2019.2957187","volume":"31","author":"HG Jung","year":"2020","unstructured":"Jung HG, Lee SW (2020) Few-shot learning with geometric constraints. 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