{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:23:22Z","timestamp":1785421402061,"version":"3.56.0"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T00:00:00Z","timestamp":1725235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Early detection of Alzheimer's disease (AD) is vital for effective treatment, as interventions are most successful in the disease's early stages. Combining Magnetic Resonance Imaging (MRI) with artificial intelligence (AI) offers significant potential for enhancing AD diagnosis. However, traditional AI models often lack transparency in their decision-making processes. Explainable Artificial Intelligence (XAI) is an evolving field that aims to make AI decisions understandable to humans, providing transparency and insight into AI systems. This research introduces the Squeeze-and-Excitation Convolutional Neural Network with Random Forest (SECNN-RF) framework for early AD detection using MRI scans. The SECNN-RF integrates Squeeze-and-Excitation (SE) blocks into a Convolutional Neural Network (CNN) to focus on crucial features and uses Dropout layers to prevent overfitting. It then employs a Random Forest classifier to accurately categorize the extracted features. The SECNN-RF demonstrates high accuracy (99.89%) and offers an explainable analysis, enhancing the model's interpretability. Further exploration of the SECNN framework involved substituting the Random Forest classifier with other machine learning algorithms like Decision Tree, XGBoost, Support Vector Machine, and Gradient Boosting. While all these classifiers improved model performance, Random Forest achieved the highest accuracy, followed closely by XGBoost, Gradient Boosting, Support Vector Machine, and Decision Tree which achieved lower accuracy.<\/jats:p>","DOI":"10.3389\/frai.2024.1456069","type":"journal-article","created":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T05:10:28Z","timestamp":1725253828000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["Advanced interpretable diagnosis of Alzheimer's disease using SECNN-RF framework with explainable AI"],"prefix":"10.3389","volume":"7","author":[{"given":"Nabil M.","family":"AbdelAziz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wael","family":"Said","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed M.","family":"AbdelHafeez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asmaa H.","family":"Ali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,9,2]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"167","DOI":"10.3390\/diagnostics13010167","article-title":"CAD-ALZ: a blockwise fine-tuning strategy on convolutional model and random forest classifier for recognition of multistage alzheimer's disease","volume":"13","author":"Abbas","year":"2023","journal-title":"Diagnostics"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1456069","DOI":"10.3389\/frai.2024.1456069","article-title":"Advanced interpretable diagnosis of Alzheimer's disease using SECNN-RF framework with explainable AI","volume":"7","author":"Abdelaziz","year":"2024","journal-title":"Front. Artif. Intellig."},{"key":"B3","doi-asserted-by":"publisher","first-page":"10415","DOI":"10.1007\/s00521-021-05799-w","article-title":"A CNN based framework for classification of Alzheimer's disease","volume":"33","author":"AbdulAzeem","year":"2021","journal-title":"Neural Comp. Appl."},{"key":"B4","first-page":"61","article-title":"\u201cQuint: interpretable question answering over knowledge bases,\u201d","volume-title":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations","author":"Abujabal","year":"2017"},{"key":"B5","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","article-title":"Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)","volume":"6","author":"Adadi","year":"2018","journal-title":"IEEE Access"},{"key":"B6","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1145\/3233547.3233667","article-title":"\u201cInterpretable machine learning in healthcare,\u201d","volume-title":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","author":"Ahmad","year":"2018"},{"key":"B7","doi-asserted-by":"publisher","first-page":"7085","DOI":"10.3390\/molecules27207085","article-title":"Dad-net: classification of alzheimer's disease using adasyn oversampling technique and optimized neural network","volume":"27","author":"Ahmed","year":"2022","journal-title":"Molecules"},{"key":"B8","doi-asserted-by":"publisher","first-page":"2911","DOI":"10.3390\/s22082911","article-title":"Brain MRI analysis for Alzheimer's disease diagnosis using CNN-based feature extraction and machine learning","volume":"22","author":"AlSaeed","year":"2022","journal-title":"Sensors"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-020-01332-6","article-title":"Explainability for artificial intelligence in healthcare: a multidisciplinary perspective","volume":"20","author":"Amann","year":"2020","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"B10","doi-asserted-by":"publisher","first-page":"108102","DOI":"10.1016\/j.patcog.2021.108102","article-title":"Explainable deep learning for efficient and robust pattern recognition: a survey of recent developments","volume":"120","author":"Bai","year":"2021","journal-title":"Pattern Recognit."},{"key":"B11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13369-022-07538-2","article-title":"Hippocampus segmentation-based Alzheimer's disease diagnosis and classification of MRI images","volume":"48","author":"Balasundaram","year":"2023","journal-title":"Electronics"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1890","DOI":"10.3390\/electronics11121890","article-title":"Constructing domain ontology for alzheimer disease using deep learning based approach","volume":"11","author":"Bangyal","year":"2022","journal-title":"Electronics"},{"key":"B13","doi-asserted-by":"publisher","first-page":"9917919","DOI":"10.1155\/2021\/9917919","article-title":"A comparative analysis of machine learning algorithms to predict alzheimer's disease","volume":"2021","author":"Bari Antor","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"B14","doi-asserted-by":"publisher","first-page":"194","DOI":"10.3389\/fnagi.2019.00194","article-title":"Layer-wise relevance propagation for explaining deep neural network decisions in MRI-based Alzheimer's disease classification","volume":"11","author":"B\u00f6hle","year":"2019","journal-title":"Front. Aging Neurosci."},{"key":"B15","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/s00259-019-04538-7","article-title":"Cognitive signature of brain FDG PET based on deep learning: domain transfer from Alzheimer's disease to Parkinson's disease","volume":"47","author":"Choi","year":"2020","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"B16","doi-asserted-by":"publisher","first-page":"102003","DOI":"10.1016\/j.nicl.2019.102003","article-title":"Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation","volume":"24","author":"Eitel","year":"2019","journal-title":"NeuroImage Clin."},{"key":"B17","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.3390\/diagnostics13071216","article-title":"Accurate detection of Alzheimer's disease using lightweight deep learning model on MRI data","volume":"13","author":"El-Latif","year":"2023","journal-title":"Molecules"},{"key":"B18","doi-asserted-by":"publisher","first-page":"102199","DOI":"10.1016\/j.nicl.2020.102199","article-title":"Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease","volume":"26","author":"Giorgio","year":"2020","journal-title":"NeuroImage Clin."},{"key":"B19","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/RBME.2022.3185953","article-title":"Explainable artificial intelligence methods in combating pandemics: a systematic review","volume":"16","author":"Giuste","year":"2022","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"B20","first-page":"7132","article-title":"\u201cSqueeze-and-excitation networks,\u201d","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Hu","year":"2018"},{"key":"B21","doi-asserted-by":"publisher","first-page":"509","DOI":"10.3389\/fnins.2019.00509","article-title":"Diagnosis of Alzheimer's disease via multi-modality 3D convolutional neural network","volume":"13","author":"Huang","year":"2019","journal-title":"Front. Neurosci."},{"key":"B22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13195-021-00837-0","article-title":"Predict Alzheimer's disease using hippocampus MRI data: a lightweight 3D deep convolutional network model with visual and global shape representations","volume":"13","author":"Katabathula","year":"2021","journal-title":"Alzheimers Res. Ther."},{"key":"B23","doi-asserted-by":"publisher","first-page":"916","DOI":"10.3390\/electronics9060916","article-title":"CNN-based network intrusion detection against denial-of-service attacks","volume":"9","author":"Kim","year":"2020","journal-title":"Electronics"},{"key":"B24","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1109\/BIBM52615.2021.9669504","article-title":"\u201cInterpretable temporal graph neural network for prognostic prediction of Alzheimer's disease using longitudinal neuroimaging data,\u201d","volume-title":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","author":"Kim","year":"2021"},{"key":"B25","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1109\/TBME.2014.2372011","article-title":"Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease","volume":"62","author":"Liu","year":"2014","journal-title":"IEEE Transact. Biomed. Eng."},{"key":"B26","doi-asserted-by":"publisher","first-page":"105032","DOI":"10.1016\/j.compbiomed.2021.105032","article-title":"Deep learning based pipelines for Alzheimer's disease diagnosis: a comparative study and a novel deep-ensemble method","volume":"141","author":"Loddo","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"B27","doi-asserted-by":"publisher","first-page":"2860","DOI":"10.3390\/electronics10222860","article-title":"Multi-method analysis of medical records and MRI images for early diagnosis of dementia and Alzheimer's disease based on deep learning and hybrid methods","volume":"10","author":"Mohammed","year":"2021","journal-title":"Electronics"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2202.12678","article-title":"Deep learning, natural language processing, and Explainable Artificial Intelligence in the biomedical domain","author":"Moradi","year":"2022","journal-title":"arXiv"},{"key":"B29","doi-asserted-by":"publisher","first-page":"90319","DOI":"10.1109\/ACCESS.2021.3090474","article-title":"DEMNET: a deep learning model for early diagnosis of Alzheimer diseases and dementia from MR images","volume":"9","author":"Muruganm","year":"2021","journal-title":"IEEE Access"},{"key":"B30","first-page":"169","article-title":"\u201cIncorporating Explainable Artificial Intelligence (XAI) to aid the understanding of machine learning in the healthcare domain,\u201d","volume-title":"AICS","author":"Pawar","year":"2020"},{"key":"B31","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1172\/JCI200317522","article-title":"Series introduction: neurodegeneration: what is it and where are we?","volume":"111","author":"Przedborski","year":"2003","journal-title":"J. Clin. Invest."},{"key":"B32","doi-asserted-by":"publisher","first-page":"1219","DOI":"10.1007\/s13246-020-00924-w","article-title":"Multi-class diagnosis of Alzheimer's disease using cascaded three dimensional-convolutional neural network","volume":"43","author":"Raju","year":"2020","journal-title":"Phys. Eng. Sci. Med."},{"key":"B33","doi-asserted-by":"publisher","first-page":"3197671","DOI":"10.1109\/ACCESS.2022.3197671","article-title":"Explainable AI for healthcare 5.0: opportunities and challenges","volume":"10","author":"Saraswat","year":"2022","journal-title":"IEEE Access"},{"key":"B34","doi-asserted-by":"publisher","first-page":"2201","DOI":"10.1007\/s13369-021-06131-3","article-title":"Detecting the stages of Alzheimer's disease with pre-trained deep learning architectures","volume":"47","author":"Sava\u015f","year":"2022","journal-title":"Arab. J. Sci. Eng."},{"key":"B35","doi-asserted-by":"publisher","first-page":"1833","DOI":"10.3390\/diagnostics12081833","article-title":"HTLML: hybrid AI based model for detection of Alzheimer's disease","volume":"12","author":"Sharma","year":"2022","journal-title":"Diagnostics"},{"key":"B36","doi-asserted-by":"publisher","first-page":"4793","DOI":"10.1109\/TNNLS.2020.3027314","article-title":"A survey on explainable artificial intelligence (XAI): toward medical xai","volume":"32","author":"Tjoa","year":"2020","journal-title":"IEEE Transact. Neural Netw. Learn. Syst."},{"key":"B37","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1080\/00207454.2020.1835900","article-title":"Alzheimer's diagnosis using deep learning in segmenting and classifying 3D brain MR images","volume":"132","author":"Tuan","year":"2020","journal-title":"Int. J. Neurosci."},{"key":"B38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-74399-w","article-title":"Multimodal deep learning models for early detection of Alzheimer's disease stage","volume":"11","author":"Venugopalan","year":"2021","journal-title":"Sci. Rep."},{"key":"B39","doi-asserted-by":"publisher","first-page":"eaan6080","DOI":"10.1126\/scirobotics.aan6080","article-title":"Transparent, explainable, and accountable AI for robotics","volume":"2","author":"Wachter","year":"2017","journal-title":"Sci. Robot."},{"key":"B40","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.inffus.2021.07.016","article-title":"Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond","volume":"77","author":"Yang","year":"2022","journal-title":"Inf. Fus."},{"key":"B41","doi-asserted-by":"publisher","first-page":"237","DOI":"10.3390\/diagnostics12020237","article-title":"Applications of Explainable Artificial Intelligence in diagnosis and surgery","volume":"12","author":"Zhang","year":"2022","journal-title":"Diagnostics"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2024.1456069\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T05:10:35Z","timestamp":1725253835000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2024.1456069\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,2]]},"references-count":41,"alternative-id":["10.3389\/frai.2024.1456069"],"URL":"https:\/\/doi.org\/10.3389\/frai.2024.1456069","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,2]]},"article-number":"1456069"}}