{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T08:38:35Z","timestamp":1776847115456,"version":"3.51.2"},"reference-count":37,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":111,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>Timely and precise identification of pulmonary nodules via CT imaging plays a pivotal role in effective lung cancer screening and diagnosis. However, traditional nodule detection largely relies on radiologists\u2019 visual inspection, which is not only labor\u2010intensive but also subjective. This paper introduces a novel two\u2010stage three\u2010dimensional convolutional neural network for automated pulmonary nodule detection. In the first stage, an adaptive multiscale dual attention (AMDA) mechanism is introduced to enhance feature representation across varying spatial scales. In the second stage, a Kolmogorov\u2013Arnold layer (KAL) is integrated to strengthen nonlinear feature modeling and improve discrimination between true nodules and hard negative samples. Based on extensive experiments conducted on the LUNA16 dataset, the results indicate that the proposed framework achieves competitive performance across a wide range of false\u2010positive rates. Furthermore, cross\u2010dataset evaluation on the LIDC\u2010IDRI dataset validates the generalization capability of the proposed method. Our approach not only advances automated nodule detection but also provides valuable support for radiologists in clinical settings.<\/jats:p>","DOI":"10.1155\/int\/3401644","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T07:41:01Z","timestamp":1776843661000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced Pulmonary Nodule Detection via Two\u2010Stage 3D CNN With Multiscale Attention and Kolmogorov\u2013Arnold Activation"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7304-2278","authenticated-orcid":false,"given":"Gang","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8427-415X","authenticated-orcid":false,"given":"Donghua","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9869-4449","authenticated-orcid":false,"given":"Minmin","family":"Pei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2828-9161","authenticated-orcid":false,"given":"Qihang","family":"Zhen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1917-3681","authenticated-orcid":false,"given":"Hu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/S0140-6736(21)00312-3","article-title":"Lung Cancer","volume":"398","author":"Alesha A. 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