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This study proposes a new hybrid model that integrates the strengths of the data-efficient image transformer (DeiT) and convolutional attention network (CoAtNet) to enhance diagnostic accuracy. Our approach\u2019s first step involves preprocessing dental radiographic images to improve their quality and enhance feature extraction. The model employs a cross-attention fusion mechanism that aligns and merges feature representations from DeiT and CoAtNet, leveraging their unique capabilities to capture relevant patterns in the data. A stacking classifier, comprising base classifiers such as support vector machines (SVM), eXtreme gradient boosting (XGBoost), and multilayer perceptron (MLP), optimizes classification performance by combining predictions from multiple models. The proposed model demonstrates superior performance, achieving an accuracy of 96%, a precision of 96.5%, 96.1% for sensitivity, 96.4% for specificity, and 96.3% for Dice similarity coefficient, thus showcasing its effectiveness in the automatic diagnosis of dental diseases.<\/jats:p>","DOI":"10.1038\/s41598-025-32514-9","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:45:18Z","timestamp":1767707118000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving dental disease diagnosis using a cross attention based hybrid model of DeiT and CoAtNet"],"prefix":"10.1038","volume":"16","author":[{"given":"Naira","family":"Elazab","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nermeen","family":"Nader","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasmin","family":"Alsakar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Waleed","family":"Mohamed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Elmogy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,6]]},"reference":[{"key":"32514_CR1","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.bspc.2017.07.005","volume":"39","author":"TM Tuan","year":"2018","unstructured":"Tuan, T. 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The study utilized publicly available datasets, and all data sources are appropriately cited in the manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The code used in this study is provided as a supplementary file accompanying the manuscript. Readers can access and use this code for research and reproducibility purposes.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}],"article-number":"805"}}