{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T12:59:17Z","timestamp":1785416357072,"version":"3.56.0"},"reference-count":36,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002457","name":"Chosun University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002457","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/01zt9a375","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["U01 AG024904"],"award-info":[{"award-number":["U01 AG024904"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Department of Defense ADNI","award":["Award W81XWH-12-2-0012"],"award-info":[{"award-number":["Award W81XWH-12-2-0012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Neuroimaging is a promising modality for early AD detection, facilitating timely clinical intervention. This study proposes an enhanced deep learning framework that extracts critical AD biomarkers from structural MRI (sMRI) data acquired from the ADNI. Our novel CNN architecture integrates conventional convolutional layers with residual and skip connections for efficient feature extraction, achieving substantially lower computational cost than standard deep architectures such as VGG-16 (138 M), while remaining more parameter-intensive than highly compact architectures such as MobileNet and EfficientNet, which are designed explicitly for resource-constrained deployment. A comprehensive preprocessing pipeline converts 3D MRI scans into 2D slices through quality control (discarding slices with mean intensity &lt; 5% of the maximum), bilinear resizing to 96 \u00d7 96 pixels, normalization using training-set statistics, and data augmentation. Stratified, subject-level data partitioning combined with robust statistical validation via bootstrapping demonstrates superior multiclass classification performance across AD, early and late MCI, and cognitively normal groups compared to state-of-the-art methods. Additionally, Grad-CAM-based interpretability maps were generated to highlight disease-relevant brain regions, confirming consistent activation around the hippocampus and temporal lobe.<\/jats:p>","DOI":"10.3390\/s26134100","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T08:45:20Z","timestamp":1782722720000},"page":"4100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable 2D Deep Learning for Alzheimer\u2019s Detection from sMRI: A Lightweight Residual CNN Approach with Comprehensive Preprocessing and Stratified Data Partitioning"],"prefix":"10.3390","volume":"26","author":[{"given":"Vyshnavi","family":"Ramineni","sequence":"first","affiliation":[{"name":"Department of Information and Communication Engineering, Chosun University, Gwangju 61452, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun-Hyung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Chosun University, Gwangju 61452, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3486-8812","authenticated-orcid":false,"given":"Goo-Rak","family":"Kwon","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Chosun University, Gwangju 61452, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1159\/000362926","article-title":"Risk Factors That Affect Life Expectancy in Alzheimer\u2019s Disease: A 15-Year Follow-Up","volume":"38","author":"Wattmo","year":"2014","journal-title":"Dement. 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Disord."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1016\/j.jalz.2015.02.005","article-title":"Determinants of Societal Costs in Alzheimer\u2019s Disease: GERAS Study Baseline Results","volume":"11","author":"Dodel","year":"2015","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1002\/alz.12737","article-title":"Recommendations to Address Key Recruitment Challenges of Alzheimer\u2019s Disease Clinical Trials","volume":"19","author":"Langbaum","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e61","DOI":"10.1016\/S2666-7568(22)00289-6","article-title":"The Ethics of Revolution in Alzheimer\u2019s Research","volume":"4","author":"Daly","year":"2023","journal-title":"Lancet Healthy Longev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1984","DOI":"10.1212\/01.WNL.0000129697.01779.0A","article-title":"The Importance of Neuritic Plaques and Tangles to the Development and Evolution of AD","volume":"62","author":"Tiraboschi","year":"2004","journal-title":"Neurology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.neunet.2023.10.040","article-title":"A Comparative Study of GNN and MLP Based Machine Learning for the Diagnosis of Alzheimer\u2019s Disease Involving Data Synthesis","volume":"169","author":"Chen","year":"2024","journal-title":"Neural Netw."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4040","DOI":"10.1109\/JBHI.2023.3280823","article-title":"Improving Alzheimer\u2019s Disease Diagnosis with Multi-Modal PET Embedding Features by a 3D Multi-Task MLP-Mixer Neural Network","volume":"27","author":"Zhang","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_8","unstructured":"Gauthier, S., Rosa-Neto, P., Morais, J.A., and Webster, C. (2021). World Alzheimer Report 2021: Journey Through the Diagnosis of Dementia, Alzheimer\u2019s Disease International."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hosseini-Asl, E., Keynto, R., and El-Baz, A. (2016). Alzheimer\u2019s Disease Diagnostics by Adaptation of 3D Convolutional Network. 2016 IEEE International Conference on Image Processing (ICIP), IEEE.","DOI":"10.1109\/ICIP.2016.7532332"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.jalz.2018.02.018","article-title":"NIA-AA Research Framework: Toward a Biological Definition of Alzheimer\u2019s Disease","volume":"14","author":"Jack","year":"2018","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_11","unstructured":"(1997). Consensus Recommendations for the Postmortem Diagnosis of Alzheimer\u2019s Disease. The National Institute on Aging, and Reagan Institute Working Group on Diagnostic Criteria for the Neuropathological Assessment of Alzheimer\u2019s Disease. Neurobiol. Aging, 18, S1\u2013S2."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1016\/j.jalz.2016.06.003","article-title":"Alzheimer\u2019s Disease Neuroimaging Initiative The Effect of \u03b2-Amyloid Positivity on Cerebral Metabolism in Cognitively Normal Seniors","volume":"12","author":"Bozoki","year":"2016","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.neurobiolaging.2017.01.014","article-title":"APOE \u03954 Status Is Associated with White Matter Hyperintensities Volume Accumulation Rate Independent of AD Diagnosis","volume":"53","author":"Sudre","year":"2017","journal-title":"Neurobiol. Aging"},{"key":"ref_14","first-page":"30","article-title":"Diagnosis of Alzheimer\u2019s Disease Using Wrapper Feature Selection Method","volume":"12","author":"Ramineni","year":"2023","journal-title":"Smart Media J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1561\/2000000039","article-title":"Deep Learning: Methods and Applications","volume":"7","author":"Deng","year":"2014","journal-title":"Found. Trends Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_17","unstructured":"Antiga, L.P.G. (2020). Deep Learning with PyTorch, Manning."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"22544","DOI":"10.1038\/s41598-021-01681-w","article-title":"Effect of Data Leakage in Brain MRI Classification Using 2D Convolutional Neural Networks","volume":"11","author":"Yagis","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"100223","DOI":"10.1016\/j.health.2023.100223","article-title":"An Alzheimer\u2019s Disease Classification Method Using Fusion of Features from Brain Magnetic Resonance Image Transforms and Deep Convolutional Networks","volume":"4","author":"Kalbkhani","year":"2023","journal-title":"Healthc. Anal."},{"key":"ref_20","unstructured":"(2025, April 09). Visualizing Higher-Layer Features of a Deep Network. Available online: https:\/\/www.researchgate.net\/publication\/265022827_Visualizing_Higher-Layer_Features_of_a_Deep_Network."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A Survey on Deep Learning in Medical Image Analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Li, F.-F. (2009). ImageNet: A Large-Scale Hierarchical Image Database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014). Going Deeper with Convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_25","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations, University of Oxford."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s40708-018-0080-3","article-title":"Brain MRI Analysis for Alzheimer\u2019s Disease Diagnosis Using an Ensemble System of Deep Convolutional Neural Networks","volume":"5","author":"Islam","year":"2018","journal-title":"Brain Inform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.cogsys.2018.12.015","article-title":"Convolutional Neural Network Based Alzheimer\u2019s Disease Classification from Magnetic Resonance Brain Images","volume":"57","author":"Jain","year":"2019","journal-title":"Cogn. Syst. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"116459","DOI":"10.1016\/j.neuroimage.2019.116459","article-title":"A Multi-Model Deep Convolutional Neural Network for Automatic Hippocampus Segmentation and Classification in Alzheimer\u2019s Disease","volume":"208","author":"Liu","year":"2020","journal-title":"NeuroImage"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106032","DOI":"10.1016\/j.cmpb.2021.106032","article-title":"Alzheimer\u2019s Disease Detection Using Depthwise Separable Convolutional Neural Networks","volume":"203","author":"Liu","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xu, Z., Deng, H., Liu, J., and Yang, Y. (2021). Diagnosis of Alzheimer\u2019s Disease Based on the Modified Tresnet. Electronics, 10.","DOI":"10.3390\/electronics10161908"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"106930","DOI":"10.1016\/j.compbiomed.2023.106930","article-title":"Effective Detection of Alzheimer\u2019s Disease by Optimizing Fuzzy K-Nearest Neighbors Based on Salp Swarm Algorithm","volume":"159","author":"Lu","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rani, P., Sandhu, J.K., and Sahu, R. (2024). A Survey of Machine Learning Techniques for Prediction of Alzheimer\u2019s Disease. 2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM), IEEE.","DOI":"10.1109\/ICIPTM59628.2024.10563680"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1247430","DOI":"10.1155\/2018\/1247430","article-title":"Classification of Alzheimer\u2019s and MCI Patients from Semantically Parcelled PET Images: A Comparison between AV45 and FDG-PET","volume":"2018","author":"Nozadi","year":"2018","journal-title":"Int. J. Biomed. Imaging"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"21","DOI":"10.33851\/JMIS.2022.9.1.21","article-title":"A Binary Classifier Using Fully Connected Neural Network for Alzheimer\u2019s Disease Classification","volume":"9","author":"Prajapati","year":"2022","journal-title":"J. Multimed. Inf. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. 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