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Although multi-view strategies can capture complementary anatomical information, conventional fusion methods often fail to account for lesion-specific features and differences in the contribution of individual views. To address these limitations, we propose a tri-view pulmonary nodule classification framework that combines Lesion-Guided Attention (LGA) with View-Weighted Fusion (VWF). The framework extracts complementary representations from axial, coronal, and sagittal slices. LGA introduces lesion masks as weak spatial priors to refine lesion-related feature responses while preserving contextual information, whereas VWF learns sample-dependent weights to adaptively aggregate the three view representations. The framework was evaluated on 869 nodules (448 benign and 421 malignant) derived from the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset. It achieved an accuracy of 93.08%, an AUC of 97.06%, and an F1-score of 92.68%, representing the highest values for these three metrics among the models included in the baseline comparison. Ablation analysis further showed that the combined use of LGA and VWF provided the best overall balance among the evaluated configurations. In the displayed test cases, Gradient-weighted Class Activation Mapping visualizations showed that the learned responses were concentrated mainly within or adjacent to lesion-relevant regions. These findings support the potential value of combining lesion-aware feature refinement with adaptive multi-view fusion for pulmonary nodule classification within the evaluated LIDC-IDRI cohort.<\/jats:p>","DOI":"10.1371\/journal.pone.0357963","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T18:05:40Z","timestamp":1789495540000},"page":"e0357963","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-view attention-based deep learning for benign\u2013malignant classification of pulmonary nodules"],"prefix":"10.1371","volume":"21","author":[{"given":"Lijun","family":"Zhang","sequence":"first","affiliation":[{"name":"Jishou University","place":["Zhangjiajie, Hunan, China"],"department":["School of Computer Science and Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debing","family":"Zhuo","sequence":"additional","affiliation":[{"name":"Jishou University","place":["Zhangjiajie, Hunan, China"],"department":["College of Intelligent Construction"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolu","family":"Wu","sequence":"additional","affiliation":[{"name":"Jishou University","place":["Zhangjiajie, Hunan, China"],"department":["School of Computer Science and Engineering"]},{"name":"Jishou University","place":["Zhangjiajie, Hunan, China"],"department":["College of Intelligent Construction"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchou","family":"He","sequence":"additional","affiliation":[{"name":"Jishou University","place":["Zhangjiajie, Hunan, China"],"department":["School of Computer Science and Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9847-2695","authenticated-orcid":true,"given":"Guodong","family":"Kang","sequence":"additional","affiliation":[{"name":"Jishou University","place":["Zhangjiajie, Hunan, China"],"department":["School of Computer Science and Engineering"]},{"name":"Jishou University","place":["Zhangjiajie, Hunan, China"],"department":["College of Intelligent Construction"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"issue":"3","key":"pone.0357963.ref001","first-page":"209","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"H Sung","year":"2021","journal-title":"CA Cancer J Clin"},{"issue":"9","key":"pone.0357963.ref002","doi-asserted-by":"crossref","first-page":"170070","DOI":"10.1098\/rsob.170070","article-title":"Progress and prospects of early detection in lung cancer","volume":"7","author":"S Blandin Knight","year":"2017","journal-title":"Open Biol"},{"issue":"5","key":"pone.0357963.ref003","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1056\/NEJMoa1102873","article-title":"Reduced lung-cancer mortality with low-dose computed tomographic screening","volume":"365","author":"National Lung Screening Trial Research Team","year":"2011","journal-title":"N Engl J Med"},{"issue":"2","key":"pone.0357963.ref004","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.231988","article-title":"A 20-year follow-up of the International Early Lung Cancer Action Program (I-ELCAP)","volume":"309","author":"CI Henschke","year":"2023","journal-title":"Radiology"},{"key":"pone.0357963.ref005","doi-asserted-by":"crossref","first-page":"104806","DOI":"10.1016\/j.compbiomed.2021.104806","article-title":"A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning","volume":"137","author":"Y Gu","year":"2021","journal-title":"Comput Biol Med"},{"key":"pone.0357963.ref006","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/j.patcog.2016.05.029","article-title":"Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification","volume":"61","author":"W Shen","year":"2017","journal-title":"Pattern Recognit"},{"key":"pone.0357963.ref007","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. 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