{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T15:18:52Z","timestamp":1780067932075,"version":"3.54.0"},"reference-count":38,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T00:00:00Z","timestamp":1764288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Information & Communications Technology Planning & Evaluation","award":["IITP-2026-RS-2022-00156334"],"award-info":[{"award-number":["IITP-2026-RS-2022-00156334"]}]},{"name":"Liaoning Provincial Department of Science and Technology Plan Project-General Project","award":["2025-MS-141"],"award-info":[{"award-number":["2025-MS-141"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) is a progressive neurodegenerative disorder characterized by subtle structural changes in the brain, which can be observed through MRI scans. Although traditional diagnostic approaches rely on clinical and neuropsychological assessments, deep learning-based methods such as 3D convolutional neural networks (CNNs) have recently been introduced to improve diagnostic accuracy. However, their high computational complexity remains a challenge. To address this, we propose a lightweight magnetic resonance imaging (MRI) classification framework that integrates adaptive multi-scale feature extraction with structural pruning and parameter optimization. The pruned model achieving a compact architecture with approximately 490k parameters (0.49 million), 4.39 billion floating-point operations, and a model size of 1.9 MB, while maintaining high classification performance across three binary tasks. The proposed framework was evaluated on the Alzheimer\u2019s Disease Neuroimaging Initiative dataset, a widely used benchmark for AD research. Notably, the model achieves a performance density(PD) of 189.87, where PD is a custom efficiency metric defined as the classification accuracy per million parameters (% pm), which is approximately 70\u00d7 higher than the basemodel, reflecting its balance between accuracy and computational efficiency. Experimental results demonstrate that the proposed framework significantly reduces resource consumption without compromising diagnostic performance, providing a practical foundation for real-time and resource-constrained clinical applications in Alzheimer\u2019s disease detection.<\/jats:p>","DOI":"10.3390\/jimaging11120426","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T15:02:48Z","timestamp":1764774168000},"page":"426","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Lightweight 3D CNN for MRI Analysis in Alzheimer\u2019s Disease: Balancing Accuracy and Efficiency"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4053-7166","authenticated-orcid":false,"given":"Kerang","family":"Cao","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110000, China"},{"name":"Key Laboratory of Intelligent Technology of Chemical Process Industry in Liaoning Province, Shenyang 110142, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0659-1066","authenticated-orcid":false,"given":"Zhongqing","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110000, China"},{"name":"Key Laboratory of Intelligent Technology of Chemical Process Industry in Liaoning Province, Shenyang 110142, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengkui","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110000, China"},{"name":"Key Laboratory of Intelligent Technology of Chemical Process Industry in Liaoning Province, Shenyang 110142, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaming","family":"Du","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110000, China"},{"name":"Key Laboratory of Intelligent Technology of Chemical Process Industry in Liaoning Province, Shenyang 110142, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0799-2654","authenticated-orcid":false,"given":"Lele","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110000, China"},{"name":"Key Laboratory of Intelligent Technology of Chemical Process Industry in Liaoning Province, Shenyang 110142, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7607-1126","authenticated-orcid":false,"given":"Hoekyung","family":"Jung","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Paichai University, Daejeon 35345, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6002-3239","authenticated-orcid":false,"given":"Minghui","family":"Geng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110000, China"},{"name":"Key Laboratory of Intelligent Technology of Chemical Process Industry in Liaoning Province, Shenyang 110142, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e70025","DOI":"10.1002\/hsr2.70025","article-title":"Deep learning techniques for Alzheimer\u2019s disease detection in 3D imaging: A systematic review","volume":"7","author":"Awang","year":"2024","journal-title":"Health Sci. 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