{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T13:20:31Z","timestamp":1791552031326,"version":"4.3.4"},"reference-count":46,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"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>Alzheimer\u2019s disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer\u2019s disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, we discuss the segmentation and classification of the Magnetic resonance imaging (MRI) of Alzheimer\u2019s disease, through the concept of transfer learning and customizing of the convolutional neural network (CNN) by specifically using images that are segmented by the Gray Matter (GM) of the brain. Instead of training and computing the proposed model accuracy from the start, we used a pre-trained deep learning model as our base model, and, after that, transfer learning was applied. The accuracy of the proposed model was tested over a different number of epochs, 10, 25, and 50. The overall accuracy of the proposed model was 97.84%.<\/jats:p>","DOI":"10.3390\/diagnostics13040801","type":"journal-article","created":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T02:30:35Z","timestamp":1676946635000},"page":"801","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["Alzheimer Disease Classification through Transfer Learning Approach"],"prefix":"10.3390","volume":"13","author":[{"given":"Noman","family":"Raza","sequence":"first","affiliation":[{"name":"Department of Computer Science, National University of Computer and Emerging Sciences, Lahore 54770, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asma","family":"Naseer","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National University of Computer and Emerging Sciences, Lahore 54770, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3023-6706","authenticated-orcid":false,"given":"Maria","family":"Tamoor","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Forman Christian College, Lahore 54600, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8139-2269","authenticated-orcid":false,"given":"Kashif","family":"Zafar","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National University of Computer and Emerging Sciences, Lahore 54770, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113320","DOI":"10.1016\/j.ejmech.2021.113320","article-title":"Alzheimer\u2019s disease and its treatment by different approaches: A review","volume":"216","author":"Srivastava","year":"2021","journal-title":"Eur. J. Med. Chem."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Alzheimer\u2019s Association (2015). 2015 Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dement., 11, 332\u2013384.","DOI":"10.1016\/j.jalz.2015.02.003"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alzheimer\u2019s Association (2016). 2016 Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dement., 12, 459\u2013509.","DOI":"10.1016\/j.jalz.2016.03.001"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Breijyeh, Z., and Karaman, R. (2020). Comprehensive review on Alzheimer\u2019s disease: Causes and treatment. Molecules, 25.","DOI":"10.3390\/molecules25245789"},{"key":"ref_5","first-page":"685","article-title":"The Alzheimer\u2019s disease neuroimaging initiative (ADNI): MRI methods","volume":"27","author":"Jack","year":"2008","journal-title":"J. Magn. Reson. Imaging Off. J. Int. Soc. Magn. Reson. Med."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Khan, Y.F., and Kaushik, B. (2020, January 9\u201311). Computer vision technique for neuro-image analysis in neurodegenerative diseases: A survey. Proceedings of the IEEE 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India.","DOI":"10.1109\/ESCI48226.2020.9167520"},{"key":"ref_7","unstructured":"Khan, Y.F., and Kaushik, B. Neuro-image classification for the prediction of alzheimer\u2019s disease using machine learning techniques. Proceedings of the International Conference on Machine Intelligence and Data Science Applications."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1007\/s11682-013-9272-x","article-title":"Apolipoprotein E, gender, and Alzheimer\u2019s disease: An overlooked, but potent and promising interaction","volume":"8","author":"Ungar","year":"2014","journal-title":"Brain Imaging Behav."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.jalz.2007.04.381","article-title":"Forecasting the global burden of Alzheimer\u2019s disease","volume":"3","author":"Brookmeyer","year":"2007","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5513500","DOI":"10.1155\/2021\/5513500","article-title":"Computer-aided brain tumor diagnosis: Performance evaluation of deep learner CNN using augmented brain MRI","volume":"2021","author":"Naseer","year":"2021","journal-title":"Int. J. Biomed. Imaging"},{"key":"ref_11","unstructured":"Kundaram, S.S., and Pathak, K.C. (2021). Proceedings of the 4th International Conference on Microelectronics, Computing And Communication Systems, Springer."},{"key":"ref_12","first-page":"1","article-title":"Computer-aided COVID-19 diagnosis and a comparison of deep learners using augmented CXRs","volume":"30","author":"Naseer","year":"2021","journal-title":"J. X-ray Sci. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neurobiolaging.2021.11.007","article-title":"White matter hyperintensities are associated with grey matter atrophy and cognitive decline in Alzheimer\u2019s disease and frontotemporal dementia","volume":"111","author":"Dadar","year":"2022","journal-title":"Neurobiol. Aging"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Aderghal, K., Khvostikov, A., Krylov, A., Benois-Pineau, J., Afdel, K., and Catheline, G. (2018, January 18\u201321). Classification of Alzheimer disease on imaging modalities with deep CNNs using cross-modal transfer learning. Proceedings of the 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), Karlstad, Sweden.","DOI":"10.1109\/CBMS.2018.00067"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"024503","DOI":"10.1117\/1.JMI.8.2.024503","article-title":"Disease Neuroimaging Initiative for the Alzheimer\u2019s. Convolutional neural networks for Alzheimer\u2019s disease detection on MRI images","volume":"8","author":"Ebrahimi","year":"2021","journal-title":"J. Med. Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Farooq, A., Anwar, S., Awais, M., and Rehman, S. (2017, January 18\u201320). A deep CNN based multi-class classification of Alzheimer\u2019s disease using MRI. Proceedings of the 2017 IEEE International Conference on Imaging Systems and Techniques (IST), Beijing, China.","DOI":"10.1109\/IST.2017.8261460"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.neucom.2014.09.072","article-title":"Early diagnosis of Alzheime\u2019s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images","volume":"151","author":"Khedher","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.neuroimage.2009.11.046","article-title":"Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer\u2019s disease","volume":"50","author":"Plant","year":"2010","journal-title":"Neuroimage"},{"key":"ref_19","first-page":"721","article-title":"Automatic segmentation of medical images using a novel Harris Hawk optimization method and an active contour model","volume":"29","author":"Tamoor","year":"2021","journal-title":"J. X-ray Sci. Technol."},{"key":"ref_20","first-page":"1","article-title":"Applying an adaptive Otsu-based initialization algorithm to optimize active contour models for skin lesion segmentation","volume":"30","author":"Malik","year":"2022","journal-title":"J. X-ray Sci. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/21681163.2015.1124249","article-title":"Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks","volume":"6","author":"Gao","year":"2018","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Vis."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.neucom.2018.10.100","article-title":"Fine-tuning pre-trained convolutional neural networks for gastric precancerous disease classification on magnification narrow-band imaging images","volume":"392","author":"Liu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_23","unstructured":"Naseer, A., and Zafar, K. (2022). Computational Intelligence, Wiley."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.neucom.2018.11.115","article-title":"Convolutional neural network based diagnosis of bone pathologies of proximal humerus","volume":"392","author":"Sezer","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_25","unstructured":"Kim, H. (2022, November 25). Organize Everything I Know. Available online: https:\/\/oi.readthedocs.io\/en\/latest\/computer{_}vision\/cnn\/densenet.html."},{"key":"ref_26","unstructured":"Tsang, S.H. (2022, November 25). Review: DenseNet\u2014Dense Convolutional Network (Image Classification). Available online: https:\/\/towardsdatascience.com\/review-densenet-image-classification-b6631a8ef803."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hon, M., and Khan, N.M. (2017, January 13\u201316). Towards Alzheimer\u2019s disease classification through transfer learning. Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas, MO, USA.","DOI":"10.1109\/BIBM.2017.8217822"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sarraf, S., and Tofighi, G. (2016, January 6\u20137). Deep learning-based pipeline to recognize Alzheimer\u2019s disease using fMRI data. Proceedings of the IEEE 2016 Future Technologies Conference (FTC), San Francisco, CA, USA.","DOI":"10.1109\/FTC.2016.7821697"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1109\/TBME.2014.2372011","article-title":"Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer\u2019s disease","volume":"62","author":"Liu","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1007\/s10278-019-00265-5","article-title":"Binary classification of Alzheimer\u2019s disease using sMRI imaging modality and deep learning","volume":"33","author":"Tufail","year":"2020","journal-title":"J. Digit. Imaging"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., and Feng, D. (May, January 29). Early diagnosis of Alzheimer\u2019s disease with deep learning. Proceedings of the 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), Beijing, China.","DOI":"10.1109\/ISBI.2014.6868045"},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"Mehmood, A., Maqsood, M., Bashir, M., and Shuyuan, Y. (2020). A deep Siamese convolution neural network for multi-class classification of Alzheimer disease. Brain Sci., 10.","DOI":"10.3390\/brainsci10020084"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1093\/brain\/awaa137","article-title":"Development and validation of an interpretable deep learning framework for Alzheimer\u2019s disease classification","volume":"143","author":"Qiu","year":"2020","journal-title":"Brain"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-019-1475-2","article-title":"A deep learning approach for automated diagnosis and multi-class classification of Alzheimer\u2019s disease stages using resting-state fMRI and residual neural networks","volume":"44","author":"Ramzan","year":"2020","journal-title":"J. Med. Syst."},{"key":"ref_37","unstructured":"Weiner, D.M.W. (2022, November 25). Alzheimer Disease Neuroimaging Initiative. Available online: https:\/\/adni.loni.usc.edu\/."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/s10278-015-9847-8","article-title":"Methods on skull stripping of MRI head scan images\u2014A review","volume":"29","author":"Kalavathi","year":"2016","journal-title":"J. Digit. Imaging"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/42.668699","article-title":"Fully automatic segmentation of the brain in MRI","volume":"17","author":"Atkins","year":"1998","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1016\/j.nicl.2013.05.004","article-title":"Alzheimer\u2019s Disease Neuroimaging Initiative. Accurate multimodal probabilistic prediction of conversion to Alzheimer\u2019s disease in patients with mild cognitive impairment","volume":"2","author":"Young","year":"2013","journal-title":"NeuroImage Clin."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1006\/nimg.2000.0582","article-title":"Voxel-Based Morphometry\u2014The Methods","volume":"11","author":"Ashburner","year":"2000","journal-title":"Neuroimage"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Bose, S.R., Pearline, A.S., and Bose, R. (2022). Real-Time Plant Species Recognition Using Non-Averaged Densenet-169 Deep Learning Paradigm, SSRN.","DOI":"10.2139\/ssrn.4031272"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Vulli, A., Srinivasu, P.N., Sashank, M.S.K., Shafi, J., Choi, J., and Ijaz, M.F. (2022). Fine-Tuned DenseNet-169 for Breast Cancer Metastasis Prediction Using FastAI and 1-Cycle Policy. Sensors, 22.","DOI":"10.3390\/s22082988"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"103217","DOI":"10.1016\/j.bspc.2021.103217","article-title":"Alzheimer\u2019s disease classification using pre-trained deep networks","volume":"71","author":"Shanmugam","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_45","first-page":"33","article-title":"Alzheimer\u2019s Disease: A Survey","volume":"8","author":"Chamarajan","year":"2021","journal-title":"Int. J. Artif. Intell."},{"key":"ref_46","first-page":"2384830","article-title":"Dense Convolutional Network and Its Application in Medical Image Analysis","volume":"2022","author":"Zhou","year":"2022","journal-title":"BioMed Res. Int."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/4\/801\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:37:21Z","timestamp":1760121441000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/4\/801"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,20]]},"references-count":46,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["diagnostics13040801"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13040801","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,20]]}}}