{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T12:45:59Z","timestamp":1793191559234,"version":"4.5.0"},"reference-count":49,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T00:00:00Z","timestamp":1711411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/publicdomain\/zero\/1.0\/"}],"funder":[{"DOI":"10.13039\/100010002","name":"Ministry of Education","doi-asserted-by":"publisher","award":["NRF-2021R1A6A1A13044830"],"award-info":[{"award-number":["NRF-2021R1A6A1A13044830"]}],"id":[{"id":"10.13039\/100010002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Science and ICT, Korea","award":["IITP-2023-2020-0-01819"],"award-info":[{"award-number":["IITP-2023-2020-0-01819"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Alzheimer\u2019s disease is the most prevalent form of dementia, which is a gradual condition that begins with mild memory loss and progresses to difficulties communicating and responding to the environment. Recent advancements in neuroimaging techniques have resulted in large-scale multimodal neuroimaging data, leading to an increased interest in using deep learning for the early diagnosis and automated classification of Alzheimer\u2019s disease. This study uses machine learning (ML) methods to determine the severity level of Alzheimer\u2019s disease using MRI images, where the dataset consists of four levels of severity. A hybrid of 12 feature extraction methods is used to diagnose Alzheimer\u2019s disease severity, and six traditional machine learning methods are applied, including decision tree, K-nearest neighbor, linear discrimination analysis, Na\u00efve Bayes, support vector machine, and ensemble learning methods. During training, optimization is performed to obtain the best solution for each classifier. Additionally, a CNN model is trained using a machine learning system algorithm to identify specific patterns. The accuracy of the Na\u00efve Bayes, Support Vector Machines, K-nearest neighbor, Linear discrimination classifier, Decision tree, Ensembled learning, and presented CNN architecture are 67.5%, 72.3%, 74.5%, 65.6%, 62.4%, 73.8% and, 95.3%, respectively. Based on the results, the presented CNN approach outperforms other traditional machine learning methods to find Alzheimer severity.<\/jats:p>","DOI":"10.1371\/journal.pone.0297996","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T17:25:18Z","timestamp":1711473918000},"page":"e0297996","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":11,"title":["A deeply supervised adaptable neural network for diagnosis and classification of Alzheimer\u2019s severity using multitask feature extraction"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1550-110X","authenticated-orcid":true,"given":"Mohsen","family":"Ahmadi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7275-2370","authenticated-orcid":true,"given":"Danial","family":"Javaheri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7883-8812","authenticated-orcid":true,"given":"Matin","family":"Khajavi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7706-0142","authenticated-orcid":true,"given":"Kasra","family":"Danesh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junbeom","family":"Hur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,3,26]]},"reference":[{"issue":"12","key":"pone.0297996.ref001","doi-asserted-by":"crossref","first-page":"3411","DOI":"10.1002\/hbm.22156","article-title":"Prediction of Alzheimer\u2019s disease and mild cognitive impairment using cortical morphological patterns","volume":"34","author":"CY Wee","year":"2013","journal-title":"Human Brain Mapping"},{"key":"pone.0297996.ref002","first-page":"1","article-title":"Denouements of machine learning and multimodal diagnostic classification of Alzheimer\u2019s disease","author":"B Naik","year":"2020","journal-title":"Vis. Comput. Ind. Biomed. Art"},{"key":"pone.0297996.ref003","first-page":"1","article-title":"Classification and Visualization of Alzheimer\u2019s Disease using Volumetric Convolutional Neural Network and Transfer Learning","volume":"34","author":"K Oh","year":"2019","journal-title":"Sci. Rep"},{"key":"pone.0297996.ref004","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.neuroscience.2021.01.002","article-title":"Transfer Learning Approach for Early Diagnosis of Alzheimer\u2019s Disease on MRI Images","volume":"460","author":"A Mehmood","year":"2021","journal-title":"Neuroscience"},{"key":"pone.0297996.ref005","article-title":"Construction and analysis of weighted brain networks from SICE for the study of Alzheimer\u2019s disease","volume":"11","author":"J Munilla","year":"2017","journal-title":"Front. Neuroscience"},{"key":"pone.0297996.ref006","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/S1474-4422(21)00066-1","article-title":"Clinical diagnosis of Alzheimer\u2019s disease: recommendations of the International Working Group","volume":"20","author":"B Dubois","year":"2021","journal-title":"Lancet Neurol"},{"key":"pone.0297996.ref007","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00401-011-0910-3","article-title":"National institute on aging-Alzheimer\u2019s association guidelines for the neuropathologic assessment of Alzheimer\u2019s disease: A practical approach","volume":"123","author":"TJ Montine","year":"2012","journal-title":"Acta Neuropathol"},{"key":"pone.0297996.ref008","doi-asserted-by":"crossref","first-page":"25","DOI":"10.31681\/jetol.457046","article-title":"A Research on Machine Learning Methods and Its Applications","volume":"1","author":"O Celik","year":"2018","journal-title":"J. Educ. Technol. Online Learn"},{"key":"pone.0297996.ref009","first-page":"1","article-title":"Recent advances and applications of machine learning in solid-state materials science","volume":"5","author":"J Schmidt","year":"2019","journal-title":"Comput. Mater"},{"key":"pone.0297996.ref010","first-page":"1","article-title":"Modular machine learning for Alzheimer\u2019s disease classification from retinal vasculature","volume":"11","author":"J Tian","year":"2021","journal-title":"Sci. Rep"},{"key":"pone.0297996.ref011","first-page":"1","article-title":"Alzheimer\u2019s disease prevention: From risk factors to early intervention","volume":"9","author":"M Crous-Bou","year":"2017","journal-title":"Alzheimer\u2019s Research & Therapy"},{"key":"pone.0297996.ref012","first-page":"9917919","article-title":"A Comparative Analysis of Machine Learning Algorithms to Predict Alzheimer\u2019s Disease","author":"MB Antor","year":"2021","journal-title":"J. Healthc"},{"key":"pone.0297996.ref013","doi-asserted-by":"crossref","first-page":"103042","DOI":"10.1016\/j.adhoc.2022.103042","article-title":"A new energy-efficient and temperature-aware routing protocol based on fuzzy logic for multi-WBANs","volume":"139","author":"D Javaheri","year":"2023","journal-title":"Ad Hoc Networks"},{"key":"pone.0297996.ref014","doi-asserted-by":"crossref","first-page":"00220","DOI":"10.3389\/fnagi.2019.00220","article-title":"Deep Learning in Alzheimer\u2019s Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data","volume":"11","author":"T Jo","year":"2019","journal-title":"Front. Aging Neurosci"},{"key":"pone.0297996.ref015","doi-asserted-by":"crossref","first-page":"3016","DOI":"10.3390\/jcm9093016","article-title":"Accurate Blood-Based Diagnostic Biosignatures for Alzheimer\u2019s Disease via Automated Machine Learning","author":"M Karaglani","year":"2020","journal-title":"J. Clin. Med"},{"key":"pone.0297996.ref016","doi-asserted-by":"crossref","first-page":"108669","DOI":"10.1016\/j.jneumeth.2020.108669","article-title":"Hippocampal atrophy based Alzheimer\u2019s disease diagnosis via machine learning methods","volume":"337","author":"G Uysal","year":"2020","journal-title":"J. Neurosci. Methods"},{"key":"pone.0297996.ref017","doi-asserted-by":"crossref","first-page":"105931","DOI":"10.1016\/j.sab.2020.105931","article-title":"Diagnosis of Alzheimer\u2019s disease using laser-induced breakdown spectroscopy and machine learning. Spectrochim","volume":"171","author":"R Gaudiuso","year":"2020","journal-title":"Acta\u2014Part B At. Spectrosc"},{"key":"pone.0297996.ref018","doi-asserted-by":"crossref","first-page":"119188","DOI":"10.1016\/j.saa.2020.119188","article-title":"Raman spectroscopy and machine learning for biomedical applications: Alzheimer\u2019s disease diagnosis based on the analysis of cerebrospinal fluid","volume":"48","author":"E Ryzhikova","year":"2021","journal-title":"Spectrochim. Acta\u2014Part A Mol. Biomol. Spectrosc"},{"key":"pone.0297996.ref019","first-page":"102712","article-title":"Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer\u2019s disease","author":"EE Bron","year":"2021","journal-title":"Spectrochim. Acta\u2014Part A Mol. Biomol. Spectrosc."},{"issue":"8","key":"pone.0297996.ref020","doi-asserted-by":"crossref","first-page":"350","DOI":"10.3390\/bioengineering9080350","article-title":"A machine learning method with filter-based feature selection for improved prediction of chronic kidney disease","volume":"9","author":"SA Ebiaredoh-Mienye","year":"2022","journal-title":"Bioengineering"},{"issue":"11","key":"pone.0297996.ref021","doi-asserted-by":"crossref","first-page":"1963","DOI":"10.3390\/electronics9111963","article-title":"Integrating enhanced sparse autoencoder-based artificial neural network technique and softmax regression for medical diagnosis","volume":"9","author":"SA Ebiaredoh-Mienye","year":"2020","journal-title":"Electronics"},{"issue":"21","key":"pone.0297996.ref022","doi-asserted-by":"crossref","first-page":"11127","DOI":"10.3390\/app122111127","article-title":"An interpretable machine learning approach for hepatitis b diagnosis","volume":"12","author":"G Obaido","year":"2022","journal-title":"Appl. Sci"},{"issue":"3","key":"pone.0297996.ref023","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1007\/s11571-022-09859-2","article-title":"Primate brain pattern-based automated Alzheimer\u2019s disease detection model using EEG signals","volume":"17","author":"S Dogan","year":"2023","journal-title":"Cogn Neurodyn"},{"key":"pone.0297996.ref024","doi-asserted-by":"crossref","first-page":"104828","DOI":"10.1016\/j.compbiomed.2021.104828","article-title":"Feed-forward LPQNet based automatic alzheimer\u2019s disease detection model","volume":"137","author":"E Kaplan","year":"2021","journal-title":"Comput. Biol. Med"},{"key":"pone.0297996.ref025","doi-asserted-by":"crossref","first-page":"103971","DOI":"10.1016\/j.medengphy.2023.103971","article-title":"ExHiF: Alzheimer\u2019s disease detection using exemplar histogram-based features with CT and MR images","volume":"115","author":"E Kaplan","year":"2023","journal-title":"Med. Eng. Phys"},{"key":"pone.0297996.ref026","first-page":"1","article-title":"Machine learning and novel biomarkers for the diagnosis of alzheimer\u2019s disease","volume":"12","author":"CH Chang","year":"2021","journal-title":"Int. J. Mol. Sci"},{"key":"pone.0297996.ref027","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-021-21330-0","article-title":"Machine learning identifies candidates for drug repurposing in Alzheimer\u2019s disease","volume":"12","author":"S Rodriguez","year":"2021","journal-title":"Nat. Commun"},{"key":"pone.0297996.ref028","doi-asserted-by":"crossref","first-page":"607858","DOI":"10.3389\/fnagi.2021.607858","article-title":"Machine Learning Profiling of Alzheimer\u2019s Disease Patients Based on Current Cerebrospinal Fluid Markers and Iron Content in Biofluids","volume":"13","author":"E Ficiara","year":"2021","journal-title":"Front. Aging Neurosci"},{"key":"pone.0297996.ref029","doi-asserted-by":"crossref","first-page":"576194","DOI":"10.3389\/fneur.2020.576194","article-title":"Comparison of Transfer Learning and Conventional Machine Learning Applied to Structural Brain MRI for the Early Diagnosis and Prognosis of Alzheimer\u2019s Disease","volume":"11","author":"L Nanni","year":"2020","journal-title":"Front. Neurol"},{"key":"pone.0297996.ref030","doi-asserted-by":"crossref","first-page":"83554","DOI":"10.1371\/journal.pone.0083554","article-title":"Different approaches for extracting information from the co-occurrence matrix","volume":"8","author":"L Nanni","year":"2013","journal-title":"PLoS One"},{"key":"pone.0297996.ref031","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/B978-0-12-813087-2.00007-5","article-title":"A set of texture-based methods for breast cancer response prediction in neoadjuvant Chemotherapy Treatment","author":"M Ammar","year":"2018","journal-title":"Soft Computing Based Medical Image Analysis"},{"key":"pone.0297996.ref032","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1109\/TIP.2010.2042645","article-title":"Enhanced local texture feature sets for face recognition under difficult lighting conditions","volume":"19","author":"X Tan","year":"2010","journal-title":"IEEE Trans. Image Process"},{"issue":"12","key":"pone.0297996.ref033","doi-asserted-by":"crossref","first-page":"129","DOI":"10.3390\/jimaging6120129","article-title":"Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection","volume":"6","author":"M Manzo","year":"2020","journal-title":"J. Imaging"},{"key":"pone.0297996.ref034","doi-asserted-by":"crossref","unstructured":"Mahdiraji SA, Baleghi Y, Sakhaei SM. BIBS, a New Descriptor for Melanoma\/Non-Melanoma Discrimination. Proceedings of the 26th Iranian Conference on Electrical Engineering. 2018:1397\u20131402.","DOI":"10.1109\/ICEE.2018.8472701"},{"issue":"1984","key":"pone.0297996.ref035","doi-asserted-by":"crossref","first-page":"20110534","DOI":"10.1098\/rsta.2011.0534","article-title":"Independent component analysis: Recent advances","volume":"371","author":"A Hyvarinen","year":"2013","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci"},{"key":"pone.0297996.ref036","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1007\/978-3-642-40246-3_55","article-title":"Identification using encrypted biometrics","volume":"8048","author":"M Haghighat","year":"2013","journal-title":"Proceedings of the Lecture Notes in Computer Science"},{"key":"pone.0297996.ref037","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.conengprac.2018.07.012","article-title":"Parallel PCA-KPCA for nonlinear process monitoring","volume":"80","author":"Q Jiang","year":"2018","journal-title":"Control Eng. Pract"},{"key":"pone.0297996.ref038","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1109\/34.908974","article-title":"PCA versus LDA","volume":"23","author":"AM Martinez","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"pone.0297996.ref039","first-page":"22","article-title":"An Overview of Machine Learning and its Applications","volume":"1","author":"V Selvam","year":"2015","journal-title":"Int. J. Electr. Sci. & Eng"},{"issue":"9","key":"pone.0297996.ref040","first-page":"7445","article-title":"DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clustering","volume":"34","author":"M Ahmadi","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci"},{"key":"pone.0297996.ref041","doi-asserted-by":"crossref","first-page":"11383","DOI":"10.1007\/s00521-020-05394-5","article-title":"Convolutional neural network-based models for diagnosis of breast cancer","volume":"34","author":"M Masud","year":"2020","journal-title":"Neural Comput. Appl"},{"key":"pone.0297996.ref042","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.knosys.2005.10.013","article-title":"Combining decision tree and Naive Bayes for classification","volume":"19","author":"LM Wang","year":"2006","journal-title":"Knowledge-Based Syst"},{"key":"pone.0297996.ref043","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.13005\/bpj\/1299","article-title":"Alzheimer\u2019s disease diagnosis by using dimensionality reduction based on KNN Classifier","volume":"10","author":"M Balamurugan","year":"2017","journal-title":"Biomed. Pharmacol. J"},{"key":"pone.0297996.ref044","first-page":"1","article-title":"Detection of brain lesion location in MRI images using convolutional neural network and robust PCA","author":"M Ahmadi","year":"2021","journal-title":"Int. J. Neurosci"},{"key":"pone.0297996.ref045","doi-asserted-by":"crossref","first-page":"104614","DOI":"10.1016\/j.bspc.2023.104614","article-title":"Brain tumor diagnosis using a step-by-step methodology based on courtship learning-based water strider algorithm","volume":"83","author":"W Ren","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"pone.0297996.ref046","doi-asserted-by":"crossref","first-page":"16400","DOI":"10.1109\/ACCESS.2022.3148298","article-title":"A neural network ensemble with feature engineering for improved credit card fraud detection","volume":"10","author":"E Esenogho","year":"2022","journal-title":"IEEE Access"},{"key":"pone.0297996.ref047","doi-asserted-by":"crossref","unstructured":"Mienye ID, Ainah PK, Emmanuel ID, Esenogho E. Sparse noise minimization in image classification using Genetic Algorithm and DenseNet. 2021 Conference on Information Communications Technology and Society (ICTAS). 2021:103\u2013108.","DOI":"10.1109\/ICTAS50802.2021.9395014"},{"key":"pone.0297996.ref048","article-title":"Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner","author":"V Kotu","year":"2014","journal-title":"Morgan Kaufmann"},{"key":"pone.0297996.ref049","first-page":"1","article-title":"Experimental and numerical diagnosis of fatigue foot using convolutional neural network","author":"A Sharifi","year":"2021","journal-title":"Comput. Methods Biomech. Biomed. Engin"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0297996","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T17:26:19Z","timestamp":1711473979000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0297996"}},"subtitle":[],"editor":[{"given":"Jose Gerardo","family":"Tamez-Pe\u00f1a","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,3,26]]},"references-count":49,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3,26]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0297996","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,26]]}}}