{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T23:33:42Z","timestamp":1791848022418,"version":"4.3.4"},"reference-count":50,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T00:00:00Z","timestamp":1749168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Alzheimer\u2019s disease (AD) is a progressive neurodegenerative disorder causing memory, cognitive, and behavioral decline. Early and accurate diagnosis is critical for timely treatment and management. This study proposes a novel hybrid deep learning framework, GLCM + VGG16 + FMO + CNN-LSTM, to improve AD diagnosis using MRI data. Methods: MRI images were preprocessed through normalization and noise reduction. Feature extraction combined texture features from the Gray-Level Co-occurrence Matrix (GLCM) and spatial features extracted from a pretrained VGG-16 network. Fisher Mantis Optimization (FMO) was employed for optimal feature selection. The selected features were classified using a CNN-LSTM model, capturing both spatial and temporal patterns. The MLP-LSTM model was included only for benchmarking purposes. The framework was evaluated on The ADNI and MIRIAD datasets. Results: The proposed method achieved 98.63% accuracy, 98.69% sensitivity, 98.66% precision, and 98.67% F1-score, outperforming CNN + SVM and 3D-CNN + BiLSTM by 2.4\u20133.5%. Comparative analysis confirmed FMO\u2019s superiority over other metaheuristics, such as PSO, ACO, GWO, and BFO. Sensitivity analysis demonstrated robustness to hyperparameter changes. Conclusions: The results confirm the efficacy and stability of the GLCM + VGG16 + FMO + CNN-LSTM model for accurate and early AD diagnosis, supporting its potential clinical application.<\/jats:p>","DOI":"10.3390\/diagnostics15121449","type":"journal-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T06:11:08Z","timestamp":1749190268000},"page":"1449","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Alzheimer\u2019s Disease Prediction Using Fisher Mantis Optimization and Hybrid Deep Learning Models"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9600-9883","authenticated-orcid":false,"given":"Sameer","family":"Abbas","sequence":"first","affiliation":[{"name":"Computer Engineering Department, Ankara Yildirim Beyazit University, 06010 Ankara, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9422-4974","authenticated-orcid":false,"given":"Mustafa","family":"Yeniad","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Ankara Yildirim Beyazit University, 06010 Ankara, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9875-4860","authenticated-orcid":false,"given":"Javad","family":"Rahebi","sequence":"additional","affiliation":[{"name":"Software Engineering Department, Istanbul Topkapi University, 34662 Istanbul, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,6]]},"reference":[{"key":"ref_1","unstructured":"Christina, P. (2018). The State of the Art of Dementia Research: New Frontiers, Alzheimer\u2019s Disease International."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Balagopalan, A., Eyre, B., Rudzicz, F., and Novikova, J. (2020). To BERT or not to BERT: Comparing speech and language-based approaches for Alzheimer\u2019s disease detection. arXiv.","DOI":"10.21437\/Interspeech.2020-2557"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Folego, G., Weiler, M., Casseb, R.F., Pires, R., and Rocha, A. (2020). Alzheimer\u2019s disease detection through whole-brain 3D-CNN MRI. Front. Bioeng. Biotechnol., 8.","DOI":"10.3389\/fbioe.2020.534592"},{"key":"ref_4","unstructured":"Balagopalan, A., Novikova, J., Rudzicz, F., and Ghassemi, M. (2018). The effect of heterogeneous data for Alzheimer\u2019s disease detection from speech. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.bbe.2020.02.002","article-title":"A comparative analysis of signal processing and classification methods for different applications based on EEG signals","volume":"40","author":"Khosla","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_6","unstructured":"Gray, K.R. (2012). Machine Learning for Image-Based Classification of Alzheimer\u2019s Disease, Imperial College London."},{"key":"ref_7","first-page":"5005","article-title":"Alzheimer disease detection empowered with transfer learning","volume":"70","author":"Ghazal","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liu, J., Li, M., Luo, Y., Yang, S., Li, W., and Bi, Y. (2021). Alzheimer\u2019s disease detection using depthwise separable convolutional neural networks. Comput. Methods Programs Biomed., 203.","DOI":"10.1016\/j.cmpb.2021.106032"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.phrs.2019.03.023","article-title":"Carbon nanomaterials and amyloid-beta interactions: Potentials for the detection and treatment of Alzheimer\u2019s disease?","volume":"143","author":"Mohajeri","year":"2019","journal-title":"Pharmacol. Res."},{"key":"ref_10","first-page":"0046958017713708","article-title":"Unmet needs for family caregivers of elderly people with dementia living in Italy: What do we know so far and what should we do next?","volume":"54","author":"Buono","year":"2017","journal-title":"Inq. J. Health Care Organ. Provis. Financ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3159","DOI":"10.1093\/cercor\/bhab407","article-title":"Hippocampus-and Thalamus-Related Fiber-Specific White Matter Reductions in Mild Cognitive Impairment","volume":"32","author":"Zhou","year":"2021","journal-title":"Cereb. Cortex"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3144","DOI":"10.1007\/s00415-020-09863-8","article-title":"Progression of grey and white matter brain damage in Parkinson\u2019s disease: A critical review of structural MRI literature","volume":"268","author":"Sarasso","year":"2021","journal-title":"J. Neurol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101694","DOI":"10.1016\/j.media.2020.101694","article-title":"Convolutional neural networks for classification of Alzheimer\u2019s disease: Overview and reproducible evaluation","volume":"63","author":"Wen","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"110017","DOI":"10.1016\/j.ejrad.2021.110017","article-title":"Integrated positron emission tomography\/magnetic resonance imaging in clinical diagnosis of Alzheimer\u2019s disease","volume":"145","author":"Gao","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.cell.2019.09.001","article-title":"Alzheimer disease: An update on pathobiology and treatment strategies","volume":"179","author":"Long","year":"2019","journal-title":"Cell"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ferreira, L., Sp\u00ednola, M., C\u00e2mara, J., Badia, S.B.I., and Cavaco, S. (2021, January 4\u20136). Feasibility of Pitch and Rhythm Musical Distortions as Cueing Method for People with Dementia in AR Cognitive Stimulation Tasks. Proceedings of the 2021 IEEE 9th International Conference on Serious Games and Applications for Health (SeGAH), Dubai, United Arab Emirates.","DOI":"10.1109\/SEGAH52098.2021.9551866"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Varghese, R.T., and Goswami, S.P. (2018). Assessment of Cognitive-Communicative Functions in Persons with Mild Cognitive Impairment and Dementia of Alzheimer\u2019s Type. Handbook of Research on Psychosocial Perspectives of Human Communication Disorders, IGI Global.","DOI":"10.4018\/978-1-5225-4955-0.ch014"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"34","DOI":"10.2174\/1573406413666171002120847","article-title":"New perspectives of Alzheimer disease diagnosis\u2013The most popular and future methods","volume":"14","author":"Pietrzak","year":"2018","journal-title":"Med. Chem."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bhushan, I., Kour, M., Kour, G., Gupta, S., Sharma, S., and Yadav, A. (2018). Alzheimer\u2019s disease: Causes & treatment\u2013A review. Ann. Biotechnol., 1.","DOI":"10.33582\/2637-4927\/1002"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.neuroscience.2021.01.002","article-title":"A transfer learning approach for early diagnosis of Alzheimer\u2019s disease on MRI images","volume":"460","author":"Mehmood","year":"2021","journal-title":"Neuroscience"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.neucom.2018.11.111","article-title":"Computer aided Alzheimer\u2019s disease diagnosis by an unsupervised deep learning technology","volume":"392","author":"Bi","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Thapa, S., Singh, P., Jain, D.K., Bharill, N., Gupta, A., and Prasad, M. (2020, January 19\u201324). Data-driven approach based on feature selection technique for early diagnosis of Alzheimer\u2019s disease. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9207359"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hussain, E., Hasan, M., Hassan, S.Z., Azmi, T.H., Rahman, M.A., and Parvez, M.Z. (2020, January 9\u201313). Deep learning based binary classification for alzheimer\u2019s disease detection using brain mri images. Proceedings of the 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), Kristiansand, Norway.","DOI":"10.1109\/ICIEA48937.2020.9248213"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.ijbiomac.2020.05.257","article-title":"Biosensing: The best alternative for conventional methods in detection of Alzheimer\u2019s disease biomarkers","volume":"161","author":"Mobed","year":"2020","journal-title":"Int. J. Biol. Macromol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.bbe.2021.02.006","article-title":"Application of Artificial Intelligence techniques for the detection of Alzheimer\u2019s disease using structural MRI images","volume":"41","author":"Zhao","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"115919","DOI":"10.1016\/j.trac.2020.115919","article-title":"Advances in amyloid beta oligomer detection applications in Alzheimer\u2019s disease","volume":"129","author":"Jamerlan","year":"2020","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Elnaggar, K., El-Gayar, M.M., and Elmogy, M. (2025). Depression Detection and Diagnosis Based on Electroencephalogram (EEG) Analysis: A Systematic Review. Diagnostics, 15.","DOI":"10.3390\/diagnostics15020210"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1038\/s41593-022-01166-7","article-title":"The three-dimensional landscape of cortical chromatin accessibility in Alzheimer\u2019s disease","volume":"25","author":"Bendl","year":"2022","journal-title":"Nat. Neurosci."},{"key":"ref_30","unstructured":"Batool, A., Hussain, M., Abidi, S.M.R., Amir, S., and Siddiqui, M.R.U. (2022). A brief review of big data used in healthcare organization-survey study. J. NCBAE, 1."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.mri.2015.11.009","article-title":"Feature-ranking-based Alzheimer\u2019s disease classification from structural MRI","volume":"34","author":"Beheshti","year":"2016","journal-title":"Magn. Reson. Imaging"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.compbiomed.2017.02.011","article-title":"Classification of Alzheimer\u2019s disease and prediction of mild cognitive impairment-to-Alzheimer\u2019s conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm","volume":"83","author":"Beheshti","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1016\/j.neuroimage.2011.01.008","article-title":"Multimodal classification of Alzheimer\u2019s disease and mild cognitive impairment","volume":"55","author":"Zhang","year":"2011","journal-title":"Neuroimage"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.neuroimage.2017.03.025","article-title":"Individual classification of Alzheimer\u2019s disease with diffusion magnetic resonance imaging","volume":"152","author":"Schouten","year":"2017","journal-title":"Neuroimage"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.jneumeth.2017.12.010","article-title":"Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer\u2019s disease patients: From the alzheimer\u2019s disease neuroimaging initiative (ADNI) data","volume":"302","author":"Dimitriadis","year":"2018","journal-title":"J. Neurosci. Methods"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/j.procs.2020.09.071","article-title":"Language-related features for early detection of Alzheimer disease","volume":"176","author":"Ammar","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Pashaei, E., Pashaei, E., and Aydin, N. (2025). Biomarker Identification for Alzheimer\u2019s Disease Using a Multi-Filter Gene Selection Approach. Int. J. Mol. Sci., 26.","DOI":"10.3390\/ijms26051816"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"121016","DOI":"10.1016\/j.neuroimage.2025.121016","article-title":"Adapting to evolving MRI data: A transfer learning approach for Alzheimer\u2019s disease prediction","volume":"307","author":"Turrisi","year":"2025","journal-title":"Neuroimage"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"874","DOI":"10.35940\/ijeat.C5345.029320","article-title":"Alzheimer\u2019s disease diagnosis using deep learning techniques","volume":"9","author":"Salehi","year":"2020","journal-title":"Int. J. Eng. Adv. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4127","DOI":"10.1002\/hbm.25115","article-title":"Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer\u2019s disease into dementia score: Independent validation on 8834 images from ADNI, AIBL, OASIS, and MIRIAD databases","volume":"41","author":"Popuri","year":"2020","journal-title":"Hum. Brain Mapp."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s00530-021-00797-3","article-title":"Transfer learning using freeze features for Alzheimer neurological disorder detection using ADNI dataset","volume":"28","author":"Naz","year":"2022","journal-title":"Multimed. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wolz, R., Julkunen, V., Koikkalainen, J., Niskanen, E., Zhang, D.P., Rueckert, D., Soininen, H., L\u00f6tj\u00f6nen, J., and Alzheimer\u2019s Disease Neuroimaging Initiative (2011). Multi-method analysis of MRI images in early diagnostics of Alzheimer\u2019s disease. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0025446"},{"key":"ref_43","first-page":"101823","article-title":"Real-time techno-economical operation of preserving microgrids via optimal NLMPC considering uncertainties","volume":"57","author":"Yaghoubi","year":"2024","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"DiPietro, R., and Hager, G.D. (2020). Deep learning: RNNs and LSTM. Handbook of Medical Image Computing and Computer Assisted Intervention, Elsevier.","DOI":"10.1016\/B978-0-12-816176-0.00026-0"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"AlSaeed, D., and Omar, S.F. (2022). Brain MRI analysis for Alzheimer\u2019s disease diagnosis using CNN-based feature extraction and machine learning. Sensors, 22.","DOI":"10.3390\/s22082911"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"63605","DOI":"10.1109\/ACCESS.2019.2913847","article-title":"Deep learning framework for Alzheimer\u2019s disease diagnosis via 3D-CNN and FSBi-LSTM","volume":"7","author":"Feng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_47","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995-International Conference on Neural Networks, Perth, WA, Australia."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/978-3-540-39930-8_5","article-title":"Ant colony optimization","volume":"141","author":"Maniezzo","year":"2004","journal-title":"New Optim. Tech. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s00521-017-3272-5","article-title":"Grey wolf optimizer: A review of recent variants and applications","volume":"30","author":"Faris","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"75893","DOI":"10.1007\/s11042-024-18579-0","article-title":"Bitterling fish optimization (BFO) algorithm","volume":"83","author":"Zareian","year":"2024","journal-title":"Multimed. Tools Appl."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/12\/1449\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:47:37Z","timestamp":1760032057000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/12\/1449"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,6]]},"references-count":50,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["diagnostics15121449"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15121449","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,6]]}}}