{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T20:56:24Z","timestamp":1779137784936,"version":"3.51.4"},"reference-count":32,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T00:00:00Z","timestamp":1715299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Alzheimer\u2019s disease is a common type of neurodegenerative condition characterized by progressive neural deterioration. The anatomical changes associated with individuals affected by Alzheimer\u2019s disease include the loss of tissue in various areas of the brain. Magnetic Resonance Imaging (MRI) is commonly used as a noninvasive tool to assess the neural structure of the brain for diagnosing Alzheimer\u2019s disease. In this study, an integrated Improved Fuzzy C-means method with improved watershed segmentation was employed to segment the brain tissue components affected by this disease. These segmented features were fed into a hybrid technique for classification. Specifically, a hybrid Convolutional Neural Network\u2013Long Short-Term Memory classifier with 14 layers was developed in this study. The evaluation results revealed that the proposed method achieved an accuracy of 98.13% in classifying segmented brain images according to different disease severities.<\/jats:p>","DOI":"10.3390\/a17050207","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T08:33:03Z","timestamp":1715589183000},"page":"207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Advanced Integration of Machine Learning Techniques for Accurate Segmentation and Detection of Alzheimer\u2019s Disease"],"prefix":"10.3390","volume":"17","author":[{"given":"Esraa H.","family":"Ali","sequence":"first","affiliation":[{"name":"Doctoral School of Sciences and Technologies\u2014EDST, Lebanese University, Beirut 1003, Lebanon"},{"name":"Computer Science Department, College of Science, Al-Nahrain University, Baghdad 10001, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sawsan","family":"Sadek","sequence":"additional","affiliation":[{"name":"Doctoral School of Sciences and Technologies\u2014EDST, Lebanese University, Beirut 1003, Lebanon"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9297-0028","authenticated-orcid":false,"given":"Georges Zakka","family":"El Nashef","sequence":"additional","affiliation":[{"name":"College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zaid F.","family":"Makki","sequence":"additional","affiliation":[{"name":"College of Engineering and Information Technology, Alshaab University, Baghdad 10001, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,10]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"S375","DOI":"10.3233\/JAD-141470","article-title":"Consortium for the early identification of Alzheimer\u2019s disease-Quebec detecting early preclinical Alzheimer\u2019s disease via cognition, neuropsychiatry, and neuroimaging: Qualitative review and recommendations for testing","volume":"42","author":"Belleville","year":"2014","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/978-3-030-05366-6_13","article-title":"Detection of alcoholism: An EEG hybrid features and ensemble subspace K-NN based ap-proach","volume":"11319","author":"Bavkar","year":"2019","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ali, E.H., Sadek, S., and Makki, Z.F. (2023, January 5\u20137). A Review of AI techniques using MRI Brain Images for Alzheimer\u2019s disease detection. Proceedings of the 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), Zouk Mosbeh, Lebanon.","DOI":"10.1109\/ACTEA58025.2023.10194002"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102072","DOI":"10.1016\/j.arr.2023.102072","article-title":"Advancements in computer-assisted diagnosis of Alzheimer\u2019s disease: A comprehensive survey of neuroimaging methods and AI techniques for early detection","volume":"91","author":"Shanmugavadivel","year":"2023","journal-title":"Ageing Res. Rev."},{"key":"ref_6","unstructured":"(2021, July 25). Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI). Available online: http:\/\/adni.loni.usc.edu\/."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"101929","DOI":"10.1016\/j.nicl.2019.101929","article-title":"Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations","volume":"23","author":"Wee","year":"2019","journal-title":"NeuroImage Clin."},{"key":"ref_9","unstructured":"Islam, J., and Zhang, Y. (2019). Understanding 3D CNN behavior for Alzheimer\u2019s disease diagnosis from brain PET scan. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Maqsood, M., Nazir, F., Khan, U., Aadil, F., Jamal, H., Mehmood, I., and Song, O.Y. (2019). Transfer Learning Assisted Classification and Detection of Alzheimer\u2019s Disease Stages Using 3D MRI Scans. Sensors, 19.","DOI":"10.3390\/s19112645"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.neucom.2020.05.087","article-title":"Multimodal multitask deep learning model for Alzheimer\u2019s disease progression detection based on time series data","volume":"412","author":"Abuhmed","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.neucom.2020.01.053","article-title":"Detecting Alzheimer\u2019s disease based on 4D fMRI: An exploration under deep learning framework","volume":"388","author":"Li","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"024503","DOI":"10.1117\/1.JMI.8.2.024503","article-title":"Convolutional neural networks for Alzheimer\u2019s disease detection on MRI images","volume":"8","author":"Ebrahimi","year":"2021","journal-title":"J. Med. Imaging"},{"key":"ref_14","first-page":"329","article-title":"Multiplane convolutional neural network (Mp-CNN) for Alzheimer\u2019s disease classification","volume":"15","author":"Angkoso","year":"2022","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e100485","DOI":"10.1136\/bmjhci-2021-100485","article-title":"Operationalising fairness in medical AI adoption: Detection of early Alzheimer\u2019s disease with 2D CNN","volume":"29","author":"Heising","year":"2022","journal-title":"BMJ Health Care Inform."},{"key":"ref_16","unstructured":"Oktavian, M.W., Yudistira, N., and Ridok, A. (2022). Classification of Alzheimer\u2019s disease using the convolutional neural network (CNN) with transfer learning and weighted loss. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2250016","DOI":"10.1142\/S146902682250016X","article-title":"Hybrid Nature-Inspired Algorithm for Feature Selection in Alzheimer Detection Using Brain MRI Images","volume":"21","author":"Agarwal","year":"2022","journal-title":"Int. J. Comput. Intell. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4479","DOI":"10.1007\/s00330-022-08547-3","article-title":"A diagnostic index based on quantitative susceptibility mapping and voxel-based morphometry may improve early diagnosis of Alzheimer\u2019s disease","volume":"32","author":"Sato","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"012009","DOI":"10.1088\/1742-6596\/1725\/1\/012009","article-title":"Detection of Alzheimer\u2019s disease with segmentation approach using K-Means Clustering and Watershed Method of MRI image","volume":"1725","author":"Holilah","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_20","first-page":"3523","article-title":"Image Segmentation Using Deep Learning: A Survey","volume":"44","author":"Minaee","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4127","DOI":"10.1007\/s12652-020-01792-8","article-title":"Lossless mecal image compression algorithm using etrolet transformation","volume":"12","author":"UmaMaheswari","year":"2021","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ali, E.H., Sadek, S., and Makki, Z.F. (2023, January 27\u201328). Novel Improved Fuzzy C-means Clustering for MR Image Brain Tissue Segmentation to Detect Alzheimer\u2019s Disease. Proceedings of the 2023 International Conference on Computer and Applications (ICCA), Cairo, Egypt.","DOI":"10.1109\/ICCA59364.2023.10401833"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/MCI.2018.2881643","article-title":"Fuzzy clustering: A historical perspective","volume":"14","author":"Ruspini","year":"2019","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Singh, T., Saxena, N., Khurana, M., Singh, D., Abdalla, M., and Alshazly, H. (2021). Data clustering using moth-flame optimization algorithm. Sensors, 21.","DOI":"10.3390\/s21124086"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"101993","DOI":"10.1016\/j.media.2021.101993","article-title":"Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19","volume":"70","author":"Zhong","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sirazitdinov, I., Kholiavchenko, M., Kuleev, R., and Ibragimov, B. (2019, January 8\u201311). Data augmentation for chest pathologies classification. Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy.","DOI":"10.1109\/ISBI.2019.8759573"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1109\/ACCESS.2017.2778011","article-title":"Action recognition in video sequences using deep bi-directional LSTM with CNN features","volume":"6","author":"Ullah","year":"2018","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","article-title":"Convolutional neural networks: An overview and application in radiology","volume":"9","author":"Yamashita","year":"2018","journal-title":"Insights Into Imaging"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12559-020-09773-x","article-title":"Deep learning in mining biological data","volume":"13","author":"Mahmud","year":"2021","journal-title":"Cogn. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.cogsys.2018.12.015","article-title":"ScienceDirect Convolutional neural network-based Alzheimer\u2019s disease classification from magnetic resonance brain images","volume":"57","author":"Jain","year":"2019","journal-title":"Cogn. Syst. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A review of recurrent neural networks: LSTM cells and network architectures","volume":"31","author":"Yu","year":"2019","journal-title":"Neural Comput."},{"key":"ref_32","unstructured":"Staudemeyer, R.C., and Morris, E.R. (2019). Understanding lstm\u2014A tutorial into long short-term memory recurrent neural networks. arXiv."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/5\/207\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:43:48Z","timestamp":1760107428000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/5\/207"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,10]]},"references-count":32,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["a17050207"],"URL":"https:\/\/doi.org\/10.3390\/a17050207","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,10]]}}}