{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T22:55:44Z","timestamp":1788648944164,"version":"build-2803163510"},"reference-count":60,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003968","name":"Iran National Science Foundation","doi-asserted-by":"publisher","award":["4038892"],"award-info":[{"award-number":["4038892"]}],"id":[{"id":"10.13039\/501100003968","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"\n Convolutional neural networks (CNNs) are widely recognized for their high precision in image classification. Nevertheless, the lack of transparency in these black-box models raises concerns in sensitive domains such as healthcare, where understanding the knowledge acquired to derive outcomes can be challenging. To address this concern, several strategies within the field of explainable AI (XAI) have been developed to enhance model interpretability. This study introduces a novel XAI technique, GASHAP, which integrates a genetic algorithm (GA) with SHapley Additive exPlanations (SHAP) to improve the explainability of our 3D convolutional neural network (3D-CNN) model. The model is designed to classify magnetic resonance imaging (MRI) brain scans of individuals with Alzheimer\u2019s disease and cognitively normal controls. Deep SHAP, a widely used XAI technique, facilitates the understanding of the influence exerted by various voxels on the final classification outcome (Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, 2017. 4765\u201374.\n https:\/\/doi.org\/10.5555\/3295222.3295230<\/jats:ext-link>\n ). However, voxel-level representation alone lacks interpretive clarity. Therefore, the objective of this study is to provide findings at the level of anatomically defined brain regions. Critical regions are identified by leveraging their SHAP values, followed by the application of a genetic algorithm to generate a definitive mask highlighting the most significant regions for Alzheimer\u2019s disease diagnosis (Shahamat H, Saniee Abadeh M. Brain MRI analysis using a deep learning based evolutionary approach. Neural Netw. 2020;126:218\u201334.\n https:\/\/doi.org\/10.1016\/j.neunet.2020.03.017<\/jats:ext-link>\n PMID: 32259762). The research commenced by implementing a 3D-CNN for MRI image classification. Subsequently, the GASHAP technique was applied to enhance model transparency. The final result is a brain mask that delineates the pertinent regions crucial for Alzheimer\u2019s disease diagnosis. Finally, a comparative analysis is conducted between our findings and those of previous studies.\n <\/jats:p>","DOI":"10.1371\/journal.pone.0337800","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T18:25:37Z","timestamp":1768587937000},"page":"e0337800","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":6,"title":["Alzheimer\u2019s disease prediction via an explainable CNN using genetic algorithm and SHAP values"],"prefix":"10.1371","volume":"21","author":[{"given":"Mohammad","family":"Zahedipour","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3429-8920","authenticated-orcid":true,"given":"Mohammad","family":"Saniee Abadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8909-8471","authenticated-orcid":true,"given":"Shakila","family":"Shojaei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"key":"pone.0337800.ref001","first-page":"4765","author":"SM Lundberg","year":"2017","journal-title":"A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems"},{"key":"pone.0337800.ref002","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.neunet.2020.03.017","article-title":"Brain MRI analysis using a deep learning based evolutionary approach","volume":"126","author":"H Shahamat","year":"2020","journal-title":"Neural Netw"},{"key":"pone.0337800.ref003","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.neucom.2020.04.157","article-title":"Convolutional neural networks for medical image analysis: State-of-the-art, comparisons, improvement and perspectives","volume":"444","author":"H Yu","year":"2021","journal-title":"Neurocomputing"},{"key":"pone.0337800.ref004","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.neucom.2019.04.028","article-title":"CLU-CNNs: Object detection for medical images","volume":"350","author":"Z Li","year":"2019","journal-title":"Neurocomputing"},{"key":"pone.0337800.ref005","doi-asserted-by":"crossref","first-page":"117","DOI":"10.33192\/Smj.2019.18","article-title":"The evaluation of posterior cingulate gyrus by diffusion tensor imaging in Alzheimer\u2019s disease patients compared with normal control subjects","volume":"71","author":"C Ngamsombat","year":"2019","journal-title":"Siriraj Medical Journal"},{"key":"pone.0337800.ref006","first-page":"3859","article-title":"Dynamic routing between capsules","author":"S Sabour","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"pone.0337800.ref007","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.media.2019.02.011","article-title":"Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network","volume":"54","author":"D Lu","year":"2019","journal-title":"Med Image Anal"},{"issue":"11","key":"pone.0337800.ref008","doi-asserted-by":"crossref","first-page":"3215","DOI":"10.1109\/JBHI.2020.3016306","article-title":"Multi-Receptive-Field CNN for Semantic Segmentation of Medical Images","volume":"24","author":"L Liu","year":"2020","journal-title":"IEEE J Biomed Health Inform"},{"issue":"4","key":"pone.0337800.ref009","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1007\/s00432-018-02834-7","article-title":"Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm","volume":"145","author":"PR Jeyaraj","year":"2019","journal-title":"J Cancer Res Clin Oncol"},{"key":"pone.0337800.ref010","author":"S Sarraf","year":"2016","journal-title":"DeepAD: Alzheimer\u2019s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI. Cold Spring Harbor Laboratory"},{"key":"pone.0337800.ref011","article-title":"Striving for simplicity: the all convolutional net","author":"JT Springenberg","year":"2014","journal-title":"arXiv preprint arXiv:1412.6806"},{"key":"pone.0337800.ref012","first-page":"2045","article-title":"An Interpretable Ensemble Deep Learning Model for Diabetic Retinopathy Disease Classification","volume":"2019","author":"H Jiang","year":"2019","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"pone.0337800.ref013","first-page":"1","author":"S Singh","year":"2019","journal-title":"From Chest X-Rays to Radiology Reports: A Multimodal Machine Learning Approach. In: 2019 Digital Image Computing: Techniques and Applications (DICTA)"},{"key":"pone.0337800.ref014","first-page":"9049","author":"X Wang","year":"2018","journal-title":"TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-Rays. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"pone.0337800.ref015","first-page":"9261","author":"K Yan","year":"2018","journal-title":"Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"pone.0337800.ref016","doi-asserted-by":"crossref","first-page":"102470","DOI":"10.1016\/j.media.2022.102470","article-title":"Explainable artificial intelligence (XAI) in deep learning-based medical image analysis","volume":"79","author":"BHM van der Velden","year":"2022","journal-title":"Med Image Anal"},{"key":"pone.0337800.ref017","doi-asserted-by":"crossref","first-page":"102117","DOI":"10.1016\/j.media.2021.102117","article-title":"SSMD: Semi-Supervised medical image detection with adaptive consistency and heterogeneous perturbation","volume":"72","author":"H-Y Zhou","year":"2021","journal-title":"Med Image Anal"},{"key":"pone.0337800.ref018","first-page":"3145","author":"A Shrikumar","year":"2017","journal-title":"Learning important features through propagating activation differences. In: Proceedings of the International Conference on Machine Learning (ICML)"},{"issue":"4","key":"pone.0337800.ref019","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1002\/jmri.21049","article-title":"The Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI): MRI methods","volume":"27","author":"CR Jack","year":"2008","journal-title":"J Magn Reson Imaging"},{"issue":"1","key":"pone.0337800.ref020","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.clinph.2020.09.015","article-title":"Stacked autoencoders as new models for an accurate Alzheimer\u2019s disease classification support using resting-state EEG and MRI measurements","volume":"132","author":"R Ferri","year":"2021","journal-title":"Clin Neurophysiol"},{"key":"pone.0337800.ref021","first-page":"3008","article-title":"Alzheimer\u2019s Disease Classification Using 2D Convolutional Neural Networks","volume":"2021","author":"G Liang","year":"2021","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"pone.0337800.ref022","first-page":"355","author":"X Xing","year":"2020","journal-title":"Dynamic image for 3D MRI image Alzheimer\u2019s disease classification. In: European Conference on Computer Vision (ECCV)"},{"issue":"53","key":"pone.0337800.ref023","doi-asserted-by":"crossref","first-page":"51","DOI":"10.19101\/IJACR.2021.1152001","article-title":"Multi-classification of alzheimer disease on magnetic resonance images (MRI) using deep convolutional neural network (DCNN) approaches","volume":"11","author":"SA Ajagbe","year":"2021","journal-title":"IJACR"},{"key":"pone.0337800.ref024","doi-asserted-by":"crossref","first-page":"35789","DOI":"10.1007\/s11042-020-09087-y","article-title":"A deep feature-based real-time system for Alzheimer disease stage detection","volume":"80","author":"H Nawaz","year":"2020","journal-title":"Multimed Tools Appl"},{"key":"pone.0337800.ref025","first-page":"133","author":"A Ebrahimi-Ghahnavieh","year":"2019","journal-title":"Transfer learning for Alzheimer\u2019s disease detection on MRI images. In: IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)"},{"key":"pone.0337800.ref026","first-page":"1503","author":"H Acharya","year":"2021","journal-title":"Alzheimer Disease Classification Using Transfer Learning. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC)"},{"issue":"1","key":"pone.0337800.ref027","doi-asserted-by":"crossref","first-page":"2637","DOI":"10.1038\/s41598-024-51985-w","article-title":"An explainable machine learning approach for Alzheimer\u2019s disease classification","volume":"14","author":"AS Alatrany","year":"2024","journal-title":"Sci Rep"},{"issue":"1","key":"pone.0337800.ref028","doi-asserted-by":"crossref","first-page":"2660","DOI":"10.1038\/s41598-021-82098-3","article-title":"A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer\u2019s disease","volume":"11","author":"S El-Sappagh","year":"2021","journal-title":"Sci Rep"},{"key":"pone.0337800.ref029","first-page":"945","article-title":"An explainable AI paradigm for Alzheimer\u2019s diagnosis using deep transfer learning","volume":"13","author":"D Jha","year":"2023","journal-title":"Diagnostics"},{"issue":"7","key":"pone.0337800.ref030","article-title":"Explainable AI-based Alzheimer\u2019s prediction and management using multimodal data","volume":"16","author":"M Hakim","year":"2024","journal-title":"Cureus"},{"key":"pone.0337800.ref031","first-page":"567","article-title":"Multimodal deep learning with explainable AI for Alzheimer\u2019s disease diagnosis using MRI and genetic data","volume":"98","author":"T Nguyen","year":"2024","journal-title":"Journal of Alzheimer\u2019s Disease"},{"key":"pone.0337800.ref032","first-page":"120876","article-title":"Graph neural networks with SHAP for Alzheimer\u2019s disease classification using connectivity data","volume":"302","author":"Y Zheng","year":"2025","journal-title":"NeuroImage"},{"issue":"3","key":"pone.0337800.ref033","doi-asserted-by":"crossref","first-page":"345","DOI":"10.3390\/diagnostics14030345","article-title":"An Explainable AI Paradigm for Alzheimer\u2019s Diagnosis Using Deep Transfer Learning","volume":"14","author":"T Mahmud","year":"2024","journal-title":"Diagnostics (Basel)"},{"key":"pone.0337800.ref034","first-page":"789","article-title":"SHAP for local and global interpretation in multimodal Alzheimer\u2019s disease prediction","volume":"99","author":"H Chun","year":"2024","journal-title":"Journal of Alzheimer\u2019s Disease"},{"key":"pone.0337800.ref035","first-page":"123","article-title":"SHAP-enhanced deep learning for three-class Alzheimer\u2019s disease classification","volume":"23","author":"Q Zhang","year":"2025","journal-title":"Neuroinformatics"},{"key":"pone.0337800.ref036","first-page":"1","article-title":"Explainable artificial intelligence in Alzheimer\u2019s disease classification: a systematic review","volume":"15","author":"SM Rezaeijo","year":"2023","journal-title":"Cognitive Computation"},{"issue":"7","key":"pone.0337800.ref037","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0130140","article-title":"On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation","volume":"10","author":"S Bach","year":"2015","journal-title":"PLoS One"},{"key":"pone.0337800.ref038","doi-asserted-by":"crossref","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\u2019s Disease Classification","volume":"11","author":"M B\u00f6hle","year":"2019","journal-title":"Front Aging Neurosci"},{"key":"pone.0337800.ref039","article-title":"A value for n-person games","volume-title":"Contributions to the Theory of Games","author":"LS Shapley","year":"2016"},{"issue":"1","key":"pone.0337800.ref040","doi-asserted-by":"crossref","first-page":"18095","DOI":"10.1038\/s41598-020-75167-6","article-title":"Volumetric breast density estimation on MRI using explainable deep learning regression","volume":"10","author":"BHM van der Velden","year":"2020","journal-title":"Sci Rep"},{"key":"pone.0337800.ref041","doi-asserted-by":"crossref","first-page":"8572","DOI":"10.1109\/ACCESS.2019.2963055","article-title":"Regional Multi-Scale Approach for Visually Pleasing Explanations of Deep Neural Networks","volume":"8","author":"D Seo","year":"2020","journal-title":"IEEE Access"},{"key":"pone.0337800.ref042","first-page":"419","author":"KM Sudar","year":"2022","journal-title":"Alzheimer\u2019s disease analysis using explainable artificial intelligence (XAI). In: Proceedings of the International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)"},{"key":"pone.0337800.ref043","doi-asserted-by":"crossref","first-page":"119709","DOI":"10.1016\/j.eswa.2023.119709","article-title":"An evolutionary explainable deep learning approach for Alzheimer\u2019s MRI classification","volume":"220","author":"S Shojaei","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"pone.0337800.ref044","doi-asserted-by":"crossref","first-page":"4738","DOI":"10.1109\/ACCESS.2020.3048348","article-title":"Explaining Deep Learning-Based Traffic Classification Using a Genetic Algorithm","volume":"9","author":"S Ahn","year":"2021","journal-title":"IEEE Access"},{"key":"pone.0337800.ref045","first-page":"720","author":"HA Shad","year":"2021","journal-title":"Exploring Alzheimer\u2019s disease prediction with XAI in various neural network models. In: Proceedings of the IEEE Region 10 Conference (TENCON)"},{"key":"pone.0337800.ref046","first-page":"484","article-title":"Explainable AI Points to White Matter Hyperintensities for Alzheimer\u2019s Disease Identification: a Preliminary Study","volume":"2022","author":"V Bordin","year":"2022","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"issue":"1","key":"pone.0337800.ref047","doi-asserted-by":"crossref","first-page":"82","DOI":"10.3390\/bioengineering12010082","article-title":"Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer\u2019s Disease Classification","volume":"12","author":"D Coluzzi","year":"2025","journal-title":"Bioengineering (Basel)"},{"key":"pone.0337800.ref048","first-page":"1345890","article-title":"SHAP-based interpretation of deep learning models for Alzheimer\u2019s disease classification using MRI","volume":"18","author":"M Li","year":"2024","journal-title":"Frontiers in Neuroscience"},{"key":"pone.0337800.ref049","first-page":"45678","article-title":"SHAP-guided deep learning for Alzheimer\u2019s disease progression analysis","volume":"12","author":"V Kumar","year":"2024","journal-title":"IEEE Access"},{"key":"pone.0337800.ref050","first-page":"108765","article-title":"Multimodal Alzheimer\u2019s classification using SHAP for feature importance","volume":"178","author":"S Jahan","year":"2024","journal-title":"Computer Biology and Medicine"},{"key":"pone.0337800.ref051","first-page":"123789","article-title":"SHAP-driven explainable AI for Alzheimer\u2019s disease diagnosis using multimodal neuroimaging","volume":"246","author":"S Patel","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"pone.0337800.ref052","first-page":"102876","article-title":"Multimodal XAI for Alzheimer\u2019s disease diagnosis using SHAP and decision trees","volume":"158","author":"Q Yang","year":"2024","journal-title":"Artificial Intelligence in Medicine"},{"key":"pone.0337800.ref053","first-page":"123","article-title":"Explainable deep learning for Alzheimer\u2019s disease using multimodal neuroimaging and SHAP","volume":"19","author":"E Thompson","year":"2025","journal-title":"Brain Imaging and Behavior"},{"issue":"1","key":"pone.0337800.ref054","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s40708-024-00222-1","article-title":"Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer\u2019s disease detection","volume":"11","author":"V Vimbi","year":"2024","journal-title":"Brain Inform"},{"key":"pone.0337800.ref055","first-page":"1345890","article-title":"An explainable artificial intelligence approach to brain connectivity in Alzheimer\u2019s disease","volume":"18","author":"A Lombardi","year":"2024","journal-title":"Frontiers in Neuroscience"},{"key":"pone.0337800.ref056","doi-asserted-by":"crossref","first-page":"120695","DOI":"10.1016\/j.neuroimage.2024.120695","article-title":"Predicting changes in brain metabolism and progression from mild cognitive impairment to dementia using multitask Deep Learning models and explainable AI","volume":"297","author":"Alzheimer\u2019s Disease Neuroimaging Initiative","year":"2024","journal-title":"Neuroimage"},{"key":"pone.0337800.ref057","first-page":"2345","article-title":"Grad-CAM and LIME for explainable Alzheimer\u2019s disease diagnosis in 3D CNNs","volume":"43","author":"S Ahmed","year":"2024","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"pone.0337800.ref058","first-page":"1234","article-title":"Explainable AI with SHAP and Grad-CAM for early Alzheimer\u2019s detection in MRI scans","volume":"14","author":"C Martinez","year":"2024","journal-title":"Diagnostics"},{"key":"pone.0337800.ref059","first-page":"012345","article-title":"Layer-wise relevance propagation for interpretable Alzheimer\u2019s disease classification on MRI","volume":"12","author":"A Gupta","year":"2025","journal-title":"Journal of Medical Imaging"},{"key":"pone.0337800.ref060","first-page":"108987","article-title":"Integrated XAI framework using SHAP and LIME for Alzheimer\u2019s disease classification","volume":"180","author":"M Hassan","year":"2025","journal-title":"Computer Biology and Medicine"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0337800","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T18:25:47Z","timestamp":1768587947000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0337800"}},"subtitle":[],"editor":[{"given":"Pradeep Kumar","family":"Gupta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,1,16]]},"references-count":60,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1,16]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0337800","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,16]]}}}