{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T13:19:00Z","timestamp":1779196740784,"version":"3.51.4"},"reference-count":41,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2024,9,16]]},"abstract":"<jats:p>The Alzheimer disease (AD) is a neurologic brain condition, which affects the cells in the brain and eventually renders a patient incapable of performing routine daily tasks. Due to the outstanding spatial clarity, high access, and strong contrast, MRI has been utilized in analyses pertaining to AD. This work develops an AD classification model using MRI images. Here, preprocessing is done by the Gabor filter. Subsequently, the Improved U-net segmentation model is employed for image segmentation. The features extracted comprises of modified LGXP features, LTP features, and LBP features as well. Finally, the Deep ensemble classifier (DEC) model is proposed for AD classification which combines classifiers such as RNN, DBN, and Deep Maxout Network (DMN). For enhancing the efficiency for classification of AD, the optimal weight of DMN is adjusted using the Self Customized BWO (SC-BWO) model. The outputs from DEC are averaged and the final result is obtained. Finally, the analysis of dice, Jaccard scores is performed to show the betterment of the SC-BWO scheme.<\/jats:p>","DOI":"10.3233\/idt-230524","type":"journal-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T11:25:15Z","timestamp":1717759515000},"page":"2537-2557","source":"Crossref","is-referenced-by-count":1,"title":["Alzheimer\u2019s disease (AD) classification using MRI: A deep ensemble model with modified local pattern feature set"],"prefix":"10.1177","volume":"18","author":[{"given":"Rajasree","family":"RS","sequence":"first","affiliation":[{"name":"Bharath Institute of Higher Education and Research, Chennai, India"},{"name":"Department of Artificial Intelligence and Machine Learning, New Horizon College of Engineering, Banglore, Karnataka, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shailaja V.","family":"Pede","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reena","family":"Kharat","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pooja Sharma","family":"S","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, D Y Patil University Ambi, Pune, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gopika","family":"GS","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suyoga","family":"Bansode","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, TPCT\u2019s College of Engineering, Osmanabad, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"4","key":"10.3233\/IDT-230524_ref1","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1109\/TPAMI.2018.2889096","article-title":"Hierarchical fully convolutional network for joint atrophy localization and Alzheimer\u2019s disease diagnosis using structural MRI","volume":"42","author":"Lian","year":"2018","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence."},{"key":"10.3233\/IDT-230524_ref2","doi-asserted-by":"crossref","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":"Murugan","year":"2021","journal-title":"Ieee Access."},{"key":"10.3233\/IDT-230524_ref3","doi-asserted-by":"crossref","first-page":"214646","DOI":"10.1109\/ACCESS.2020.3040340","article-title":"A novel convolutional variation of broad learning system for Alzheimer\u2019s disease diagnosis by using MRI images","volume":"8","author":"Han","year":"2020","journal-title":"IEEE Access."},{"key":"10.3233\/IDT-230524_ref4","doi-asserted-by":"crossref","first-page":"29870","DOI":"10.1109\/ACCESS.2021.3059658","article-title":"Volumetric feature-based Alzheimer\u2019s disease diagnosis from sMRI data using a convolutional neural network and a deep neural network","volume":"9","author":"Basher","year":"2021","journal-title":"IEEE Access."},{"key":"10.3233\/IDT-230524_ref5","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/LSP.2020.2964161","article-title":"Combining of multiple deep networks via ensemble generalization loss, based on MRI images, for Alzheimer\u2019s disease classification","volume":"27","author":"Choi","year":"2020","journal-title":"IEEE Signal Processing Letters."},{"issue":"8","key":"10.3233\/IDT-230524_ref6","doi-asserted-by":"crossref","first-page":"3918","DOI":"10.1109\/JBHI.2022.3155705","article-title":"A multi-stream convolutional neural network for classification of progressive MCI in Alzheimer\u2019s disease using structural MRI images","volume":"26","author":"Ashtari-Majlan","year":"2022","journal-title":"IEEE Journal of Biomedical and Health Informatics."},{"key":"10.3233\/IDT-230524_ref7","doi-asserted-by":"crossref","first-page":"112117","DOI":"10.1109\/ACCESS.2022.3216393","article-title":"Data Complexity Based Evaluation of the Model Dependence of Brain MRI Images for Classification of Brain Tumor and Alzheimer\u2019s Disease","volume":"10","author":"Kujur","year":"2022","journal-title":"IEEE Access."},{"key":"10.3233\/IDT-230524_ref8","doi-asserted-by":"crossref","first-page":"65055","DOI":"10.1109\/ACCESS.2022.3180073","article-title":"Automated detection of Alzheimer\u2019s disease and mild cognitive impairment using whole brain MRI","volume":"10","author":"Faisal","year":"2022","journal-title":"IEEE Access."},{"key":"10.3233\/IDT-230524_ref10","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.neurobiolaging.2019.12.001","article-title":"Dysregulated Fc gamma receptor-mediated phagocytosis pathway in Alzheimer\u2019s disease: Network-based gene expression analysis","volume":"88","author":"Park","year":"2020","journal-title":"Neurobiology of Aging."},{"key":"10.3233\/IDT-230524_ref11","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.neurobiolaging.2020.01.005","article-title":"Choroid plexus volume is associated with levels of CSF proteins: Relevance for Alzheimer\u2019s and Parkinson\u2019s disease","volume":"89","author":"Tadayon","year":"2020","journal-title":"Neurobiology of Aging."},{"key":"10.3233\/IDT-230524_ref12","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.neurobiolaging.2019.12.022","article-title":"Cognitive reserve predicts future executive function decline in older adults with Alzheimer\u2019s disease pathology but not age-associated pathology","volume":"88","author":"McKenzie","year":"2020","journal-title":"Neurobiology of Aging."},{"issue":"1","key":"10.3233\/IDT-230524_ref13","first-page":"679","article-title":"Plasma neurofilament light associates with Alzheimer\u2019s disease metabolic decline in amyloid-positive individuals","volume":"11","author":"Benedet","year":"2019","journal-title":"Alzheimer\u2019s & Dementia: Diagnosis, Assessment & Disease Monitoring."},{"key":"10.3233\/IDT-230524_ref14","doi-asserted-by":"crossref","first-page":"105348","DOI":"10.1016\/j.cmpb.2020.105348","article-title":"A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer\u2019s disease","volume":"189","author":"Mart\u00ed-Juan","year":"2020","journal-title":"Computer Methods and Programs in Biomedicine."},{"key":"10.3233\/IDT-230524_ref15","doi-asserted-by":"crossref","first-page":"102023","DOI":"10.1016\/j.simpat.2019.102023","article-title":"A new machine learning method for identifying Alzheimer\u2019s disease","volume":"99","author":"Liu","year":"2020","journal-title":"Simulation Modelling Practice and Theory."},{"key":"10.3233\/IDT-230524_ref16","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.inffus.2020.01.001","article-title":"Autosomal dominantly inherited alzheimer disease: Analysis of genetic subgroups by machine learning","volume":"58","author":"Castillo-Barnes","year":"2020","journal-title":"Information Fusion."},{"issue":"4","key":"10.3233\/IDT-230524_ref17","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.gpb.2019.09.004","article-title":"Machine learning to detect Alzheimer\u2019s disease from circulating non-coding RNAs","volume":"17","author":"Ludwig","year":"2019","journal-title":"Genomics, Proteomics & Bioinformatics."},{"issue":"6","key":"10.3233\/IDT-230524_ref18","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1016\/j.neuron.2019.12.031","article-title":"Human herpesvirus 6 detection in Alzheimer\u2019s disease cases and controls across multiple cohorts","volume":"105","author":"Allnutt","year":"2020","journal-title":"Neuron."},{"key":"10.3233\/IDT-230524_ref19","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.aca.2019.09.042","article-title":"Competitive electrochemical immunosensor for the detection of unfolded p53 protein in blood as biomarker for Alzheimer\u2019s disease","volume":"1093","author":"Amor-Guti\u00e9rrez","year":"2020","journal-title":"Analytica Chimica Acta."},{"key":"10.3233\/IDT-230524_ref20","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.neucom.2019.04.023","article-title":"Multi-stream multi-scale deep convolutional networks for Alzheimer\u2019s disease detection using MR images","volume":"350","author":"Ge","year":"2019","journal-title":"Neurocomputing."},{"key":"10.3233\/IDT-230524_ref21","doi-asserted-by":"crossref","first-page":"111175","DOI":"10.1016\/j.mad.2019.111175","article-title":"Extracellular vesicles as an emerging tool for the early detection of Alzheimer\u2019s disease","volume":"184","author":"Li","year":"2019","journal-title":"Mechanisms of Ageing and Development."},{"key":"10.3233\/IDT-230524_ref22","doi-asserted-by":"crossref","first-page":"164237","DOI":"10.1016\/j.ijleo.2020.164237","article-title":"A CAD system for diagnosing Alzheimer\u2019s disease using 2D slices and an improved AlexNet-SVM method","volume":"212","author":"Shakarami","year":"2020","journal-title":"Optik."},{"key":"10.3233\/IDT-230524_ref23","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.neuroimage.2018.05.051","article-title":"Integrating spatial-anatomical regularization and structure sparsity into SVM: Improving interpretation of Alzheimer\u2019s disease classification","volume":"178","author":"Sun","year":"2018","journal-title":"NeuroImage."},{"key":"10.3233\/IDT-230524_ref24","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.neucom.2018.09.001","article-title":"A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer\u2019s disease","volume":"320","author":"Zeng","year":"2018","journal-title":"Neurocomputing."},{"key":"10.3233\/IDT-230524_ref25","doi-asserted-by":"crossref","first-page":"101645","DOI":"10.1016\/j.nicl.2018.101645","article-title":"Automated classification of Alzheimer\u2019s disease and mild cognitive impairment using a single MRI and deep neural networks","volume":"21","author":"Basaia","year":"2019","journal-title":"NeuroImage: Clinical."},{"key":"10.3233\/IDT-230524_ref26","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.jneumeth.2019.01.011","article-title":"A novel texture extraction technique with T1 weighted MRI for the classification of Alzheimer\u2019s disease","volume":"318","author":"Vaithinathan","year":"2019","journal-title":"Journal of Neuroscience Methods."},{"key":"10.3233\/IDT-230524_ref27","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.neuroimage.2018.08.042","article-title":"Reproducible evaluation of classification methods in Alzheimer\u2019s disease: Framework and application to MRI and PET data","volume":"183","author":"Samper-Gonz\u00e1lez","year":"2018","journal-title":"NeuroImage."},{"key":"10.3233\/IDT-230524_ref28","doi-asserted-by":"crossref","first-page":"101837","DOI":"10.1016\/j.nicl.2019.101837","article-title":"Prediction of Alzheimer\u2019s disease dementia with MRI beyond the short-term: Implications for the design of predictive models","volume":"23","author":"Moscoso","year":"2019","journal-title":"NeuroImage: Clinical."},{"issue":"4","key":"10.3233\/IDT-230524_ref29","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1016\/j.jalz.2018.12.001","article-title":"Predicting diagnosis and cognition with 18F-AV-1451 tau PET and structural MRI in Alzheimer\u2019s disease","volume":"15","author":"Mattsson","year":"2019","journal-title":"Alzheimer\u2019s & Dementia."},{"key":"10.3233\/IDT-230524_ref30","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.mri.2019.06.019","article-title":"Automated atrophy assessment for Alzheimer\u2019s disease diagnosis from brain MRI images","volume":"62","author":"Shaikh","year":"2019","journal-title":"Magnetic Resonance Imaging."},{"key":"10.3233\/IDT-230524_ref31","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1016\/j.bspc.2018.08.009","article-title":"Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer\u2019s disease","volume":"52","author":"Lahmiri","year":"2019","journal-title":"Biomedical Signal Processing and Control."},{"key":"10.3233\/IDT-230524_ref32","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.neuroimage.2018.08.040","article-title":"Using high-dimensional machine learning methods to estimate an anatomical risk factor for Alzheimer\u2019s disease across imaging databases","volume":"183","author":"Casanova","year":"2018","journal-title":"Neuroimage."},{"issue":"5","key":"10.3233\/IDT-230524_ref35","first-page":"13","article-title":"Modified histogram equalization for image contrast enhancement using particle swarm optimization","volume":"1","author":"Shanmugavadivu","year":"2011","journal-title":"Int. J. Comput. Sci. Eng. IT."},{"key":"10.3233\/IDT-230524_ref36","doi-asserted-by":"crossref","first-page":"111804","DOI":"10.1016\/j.measurement.2022.111804","article-title":"Data anomaly detection for structural health monitoring by multi-view representation based on local binary patterns","volume":"202","author":"Zhang","year":"2022","journal-title":"Measurement."},{"issue":"3","key":"10.3233\/IDT-230524_ref37","first-page":"290","article-title":"Combining wavelet transform and LBP related features for fingerprint liveness detection","volume":"43","author":"Xia","year":"2016","journal-title":"IAENG International Journal of Computer Science."},{"key":"10.3233\/IDT-230524_ref38","doi-asserted-by":"crossref","first-page":"114135","DOI":"10.1109\/ACCESS.2021.3105114","article-title":"Adaptive local ternary pattern on parameter optimized-faster region convolutional neural network for pulmonary emphysema diagnosis","volume":"9","author":"Mondal","year":"2021","journal-title":"IEEE Access."},{"key":"10.3233\/IDT-230524_ref41","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.jmsy.2020.07.020","article-title":"Application of integrated recurrent neural network with multivariate adaptive regression splines on SPC-EPC process","volume":"57","author":"Kao","year":"2020","journal-title":"Journal of Manufacturing Systems"},{"issue":"4","key":"10.3233\/IDT-230524_ref44","doi-asserted-by":"crossref","first-page":"100322","DOI":"10.1016\/j.aosl.2022.100322","article-title":"U-Net: A deep-learning method for improving summer precipitation forecasts in China","volume":"16","author":"Deng","year":"2023","journal-title":"Atmospheric and Oceanic Science Letters."},{"key":"10.3233\/IDT-230524_ref46","doi-asserted-by":"crossref","first-page":"109215","DOI":"10.1016\/j.knosys.2022.109215","article-title":"Beluga whale optimization: A novel nature-inspired metaheuristic algorithm","volume":"251","author":"Zhong","year":"2022","journal-title":"Knowledge-Based Systems."},{"issue":"2","key":"10.3233\/IDT-230524_ref49","doi-asserted-by":"crossref","first-page":"200","DOI":"10.30630\/joiv.5.2.572","article-title":"3D CNN based Alzheimer\u2019s diseases classification using segmented Grey matter extracted from whole-brain MRI","volume":"5","author":"Khagi","year":"2021","journal-title":"JOIV: International Journal on Informatics Visualization."},{"key":"10.3233\/IDT-230524_ref50","first-page":"1","article-title":"Ensemble-of-classifiers-based approach for early Alzheimer\u2019s Disease detection","volume":"12","author":"Rajasree","year":"2023","journal-title":"Multimedia Tools and Applications."},{"issue":"15","key":"10.3233\/IDT-230524_ref53","doi-asserted-by":"crossref","first-page":"2489","DOI":"10.3390\/diagnostics13152489","article-title":"An efficient ensemble approach for Alzheimer\u2019s disease detection using an adaptive synthetic technique and deep learning","volume":"13","author":"Mujahid","year":"2023","journal-title":"Diagnostics."}],"container-title":["Intelligent Decision Technologies"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDT-230524","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:24:02Z","timestamp":1777454642000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDT-230524"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,16]]},"references-count":41,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/idt-230524","relation":{},"ISSN":["1872-4981","1875-8843"],"issn-type":[{"value":"1872-4981","type":"print"},{"value":"1875-8843","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,16]]}}}