{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T21:34:35Z","timestamp":1768599275550,"version":"3.49.0"},"reference-count":34,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Princess Nourah bint Abdulrahman University Researchers","award":["PNURSP2025R300"],"award-info":[{"award-number":["PNURSP2025R300"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Alzheimer\u2019s disease (AD) is a progressive neurodegenerative disorder that leads to a decline in memory and cognitive functions. An early and accurate diagnosis is critical for effective management and treatment also tend to lack sufficient accuracy. Accurate and early discrimination of Alzheimer\u2019s disease (AD) remains a critical challenge in medical imaging and computational neuroscience. Traditional diagnostic approaches, such as clinical assessments and neuroimaging, are often subjective and labor-intensive work. Recently, convolutional neural networks (CNNs) and handcrafted feature extraction techniques have shown promising results for automated AD classification. In order to improve AD detection accuracy, this study suggests a novel and efficient hybrid feature extraction method to address the accurate detection of Alzheimer\u2019s disease (AD). This method combines deep feature representations taken from customized CNN with the Generalized Hadamard Difference (GHD) operator, a mathematical technique that is intended to capture subtle structural variations. This integrated approach leverages the complementary strengths of handcrafted and learned features to better characterize the complex patterns associated with AD progression. The publicly accessible OASIS-1 MRI dataset was employed to rigorously evaluate the proposed method, consisting of high-resolution T1-weighted brain images from cognitively normal and impaired subjects. Classification was performed using a Support Vector Machine (SVM) classifier, yielding an impressive overall accuracy of 99.50\u202f%, surpassing many existing state-of-the-art methods. The results highlight that incorporating GHD with deep features improved the method\u2019s ability to detect early and subtle manifestations of\u00a0AD.<\/jats:p>","DOI":"10.1515\/jisys-2025-0128","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T10:17:11Z","timestamp":1768558631000},"source":"Crossref","is-referenced-by-count":0,"title":["Discrimination of Alzheimer\u2019s diseases based on combination of generalized Hadamard difference and deep learning features"],"prefix":"10.1515","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2449-1516","authenticated-orcid":false,"given":"Ala\u2019a R.","family":"Al-Shamasneh","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer & Information Sciences , Prince Sultan University , Rafha Street , Riyadh 11586 , Saudi Arabia"}]},{"given":"Herman Khalid","family":"Omer","sequence":"additional","affiliation":[{"name":"Information Technology Department, Technical College of Duhok , Duhok Polytechnic University , 1006 , Duhok , Iraq"}]},{"given":"Faten Khalid","family":"Karim","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences , Princess Nourah Bint Abdulrahman University , P.O Box\u00a084428 , Riyadh 11671 , Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4823-6851","authenticated-orcid":false,"given":"Hamid A.","family":"Jalab","sequence":"additional","affiliation":[{"name":"Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University , 64001 , Thi Qar , Iraq"}]}],"member":"374","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"key":"2026011610170721141_j_jisys-2025-0128_ref_001","doi-asserted-by":"crossref","unstructured":"Y. J. Choi et al., \u201cFive items differentiate mild to severe dementia from normal to minimal cognitive impairment\u00a0\u2013\u00a0Using the global deterioration scale,\u201d J.\u00a0Clin. Gerontol. Geriatr., vol.\u00a07, no. 1, pp.\u00a01\u20135, 2016, https:\/\/doi.org\/10.1016\/j.jcgg.2015.05.004.","DOI":"10.1016\/j.jcgg.2015.05.004"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_002","doi-asserted-by":"crossref","unstructured":"Y. Ding, C. Zhang, T. Lan, Z. Qin, X. Zhang, and W. Wang, \u201cClassification of Alzheimer\u2019s disease based on the combination of morphometric feature and texture feature,\u201d in 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, 2015, pp.\u00a0409\u2013412.","DOI":"10.1109\/BIBM.2015.7359716"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_003","doi-asserted-by":"crossref","unstructured":"R. W. Ibrahim, A. M. Hasan, and H. A. Jalab, \u201cA new deformable model based on fractional wright energy function for tumor segmentation of volumetric brain MRI scans,\u201d Comput. Methods Progr. Biomed., vol. 163, no. 9, pp. 21\u201328, 2018, https:\/\/doi.org\/10.1016\/j.cmpb.2018.05.031.","DOI":"10.1016\/j.cmpb.2018.05.031"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_004","doi-asserted-by":"crossref","unstructured":"S. Qiu et al., \u201cMultimodal deep learning for Alzheimer\u2019s disease dementia assessment,\u201d Nat. Commun., vol.\u00a013, no. 1, p.\u00a03404, 2022, https:\/\/doi.org\/10.1038\/s41467-022-31037-5.","DOI":"10.1038\/s41467-022-31037-5"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_005","doi-asserted-by":"crossref","unstructured":"S. Karantzoulis and J.\u00a0E. Galvin, \u201cDistinguishing Alzheimer\u2019s disease from other major forms of dementia,\u201d Expert Rev. Neurother., vol.\u00a011, no. 11, pp.\u00a01579\u20131591, 2011, https:\/\/doi.org\/10.1586\/ern.11.155.","DOI":"10.1586\/ern.11.155"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_006","doi-asserted-by":"crossref","unstructured":"A. a. R. Al-Shamasneh, H. A. Jalab, P. Shivakumara, R. W. Ibrahim, and U. H. Obaidellah, \u201cKidney segmentation in MR images using active contour model driven by fractional-based energy minimization,\u201d Signal, Image Video Process., vol. 14, no. 1, pp. 1361\u20131368, 2020, https:\/\/doi.org\/10.1007\/s11760-020-01673-9.","DOI":"10.1007\/s11760-020-01673-9"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_007","doi-asserted-by":"crossref","unstructured":"I. Aldawish and H. A. Jalab, \u201cDeep and hand-crafted features based on Weierstrass elliptic function for MRI brain tumor classification,\u201d J.\u00a0Intell. Syst., vol.\u00a033, no. 1, p.\u00a020240106, 2024, https:\/\/doi.org\/10.1515\/jisys-2024-0106.","DOI":"10.1515\/jisys-2024-0106"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_008","doi-asserted-by":"crossref","unstructured":"A. M. Hasan, F. Meziane, and M. Abd Kadhim, \u201cAutomated segmentation of tumours in MRI brain scans,\u201d in International Conference on Bioimaging, vol.\u00a03, SCITEPRESS, 2016, pp.\u00a055\u201362.","DOI":"10.5220\/0005625900550062"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_009","doi-asserted-by":"crossref","unstructured":"C. R. JackJr, R. C. Petersen, P. C. O\u2019brien, and E. G. Tangalos, \u201cMR\u2010based hippocampal volumetry in the diagnosis of Alzheimer\u2019s disease,\u201d Neurology, vol.\u00a042, no. 1, p.\u00a0183, 1992, https:\/\/doi.org\/10.1212\/wnl.42.1.183.","DOI":"10.1212\/WNL.42.1.183"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_010","doi-asserted-by":"crossref","unstructured":"S. Kl\u00f6ppel et al., \u201cAutomatic classification of MR scans in Alzheimer\u2019s disease,\u201d Brain, vol.\u00a0131, no. 3, pp.\u00a0681\u2013689, 2008, https:\/\/doi.org\/10.1093\/brain\/awm319.","DOI":"10.1093\/brain\/awm319"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_011","doi-asserted-by":"crossref","unstructured":"G. Mirzaei and H. Adeli, \u201cMachine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia,\u201d Biomed. Signal Process. Control, vol. 72, no. 1, p. 103293, 2022, https:\/\/doi.org\/10.1016\/j.bspc.2021.103293.","DOI":"10.1016\/j.bspc.2021.103293"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_012","doi-asserted-by":"crossref","unstructured":"E. Westman, J.-S. Muehlboeck, and A. 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Control, vol. 104, no. 1, p. 107546, 2025, https:\/\/doi.org\/10.1016\/j.bspc.2025.107546.","DOI":"10.1016\/j.bspc.2025.107546"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_025","doi-asserted-by":"crossref","unstructured":"H. A. Jalab, A. S. Al-Shamayleh, M. M. Abualhaj, Q. Y. Shambour, and H. K. Omer, \u201cMachine learning classification method for wheelchair detection using bag-of-visual-words technique,\u201d Disabil. Rehabil. Assist. Technol., vol. 20, no. 6, pp. 1\u201311, 2025, https:\/\/doi.org\/10.1080\/17483107.2025.2476105.","DOI":"10.1080\/17483107.2025.2476105"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_026","doi-asserted-by":"crossref","unstructured":"M. H. Al-Sheikh, O. Al Dandan, A. S. Al-Shamayleh, H. A. Jalab, and R. W. Ibrahim, \u201cMulti-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images,\u201d Sci. Rep., vol.\u00a013, no. 1, p.\u00a019373, 2023, https:\/\/doi.org\/10.1038\/s41598-023-46147-3.","DOI":"10.1038\/s41598-023-46147-3"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_027","doi-asserted-by":"crossref","unstructured":"R. W. Ibrahim, \u201cExistence and uniqueness of holomorphic solutions for fractional Cauchy problem,\u201d J.\u00a0Math. Anal. Appl., vol.\u00a0380, no. 1, pp.\u00a0232\u2013240, 2011, https:\/\/doi.org\/10.1016\/j.jmaa.2011.03.001.","DOI":"10.1016\/j.jmaa.2011.03.001"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_028","doi-asserted-by":"crossref","unstructured":"F. H. Jackson, \u201cXI.\u00a0\u2013\u00a0On q-functions and a certain difference operator,\u201d Earth Environ. Sci. Trans. Royal Soc. Edinb., vol.\u00a046, no. 2, pp.\u00a0253\u2013281, 1909, https:\/\/doi.org\/10.1017\/S0080456800002751.","DOI":"10.1017\/S0080456800002751"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_029","doi-asserted-by":"crossref","unstructured":"I. R. W. Momani Shaher, \u201cEntropy-regularized fractional diffusion via the frontiers in applied mathematics and statistics,\u201d Numer. Anal. Sci. Comput., vol. 2025, no. 1, pp. 1\u201327, 2025, https:\/\/doi.org\/10.3389\/fams.2025.1643121.","DOI":"10.3389\/fams.2025.1643121"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_030","doi-asserted-by":"crossref","unstructured":"I. R. W. Aldawish Ibtisam, \u201cDistributed-order (q, t)-Deformed l\u00e9vy processes and their spectral properties, frontiers in physics,\u201d Interdiscip. Phys., no. 2025, pp.\u00a01\u201327, 2025, https:\/\/doi.org\/10.3389\/fphy.2025.1647182.","DOI":"10.3389\/fphy.2025.1647182"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_031","unstructured":"MATLAB\n, 2025. Natick, Massachusetts, United\u00a0States. [Online]. Available: https:\/\/www.mathworks.com."},{"key":"2026011610170721141_j_jisys-2025-0128_ref_032","doi-asserted-by":"crossref","unstructured":"L. V. Fulton, D. Dolezel, J. Harrop, Y. Yan, and C. P. Fulton, \u201cClassification of Alzheimer\u2019s disease with and without imagery using gradient boosted machines and ResNet-50,\u201d Brain Sci., vol.\u00a09, no. 9, p.\u00a0212, 2019, https:\/\/doi.org\/10.3390\/brainsci9090212.","DOI":"10.3390\/brainsci9090212"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_033","doi-asserted-by":"crossref","unstructured":"A. R. W. Sait and R. Nagaraj, \u201cA feature-fusion technique-based Alzheimer\u2019s disease classification using magnetic resonance imaging,\u201d Diagnostics, vol.\u00a014, no. 21, p.\u00a02363, 2024, https:\/\/doi.org\/10.3390\/diagnostics14212363.","DOI":"10.3390\/diagnostics14212363"},{"key":"2026011610170721141_j_jisys-2025-0128_ref_034","doi-asserted-by":"crossref","unstructured":"M. Z. Hussain et al., \u201cA fine-tuned convolutional neural network model for accurate Alzheimer\u2019s disease classification,\u201d Sci. 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