{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T14:56:17Z","timestamp":1787237777405,"version":"build-2736575974"},"reference-count":42,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Microscopy Res &amp; Technique"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The disease amyloid plaques, neurofibrillary tangles, synaptic dysfunction, and neuronal death gradually accumulate throughout Alzheimer's disease (AD), resulting in cognitive decline and functional disability. The challenges of dataset quality, interpretability, ethical integration, population variety, and picture standardization must be addressed using deep learning for the functional magnetic resonance imaging (MRI) classification of AD in order to guarantee a trustworthy and practical therapeutic application. In this manuscript Classifying AD using a finite basis physics neural network (CAD\u2010FBPINN) is proposed. Initially, images are collected from AD Neuroimaging Initiative (ADNI) dataset. The images are fed to Pre\u2010processing segment. During the preprocessing phase the reverse lognormal Kalman filter (RLKF) is used to enhance the input images. Then the preprocessed images are given to the feature extraction process. Feature extraction is done by Newton\u2010time\u2010extracting wavelet transform (NTEWT), which is used to extract the statistical features such as the mean, kurtosis, and skewness. Finally the features extracted are given to FBPINNs for Classifying AD such as early mild cognitive impairment (EMCI), AD, mild cognitive impairment (MCI), late mild cognitive impairment (LMCI), normal control (NC), and subjective memory complaints (SMCs). In General, FBPINN does not express adapting optimization strategies to determine optimal factors to ensure correct AD classification. Hence, sea\u2010horse optimization algorithm (SHOA) to optimize FBPINN, which accurately classifies AD. The proposed technique implemented in python and efficacy of the CAD\u2010FBPINN technique is assessed with support of numerous performances like accuracy, precision, Recall, F1\u2010score, specificity and negative predictive value (NPV) is analyzed. Proposed CAD\u2010FBPINN method attain 30.53%, 23.34%, and 32.64% higher accuracy; 20.53%, 25.34%, and 29.64% higher precision; 20.53%, 25.34%, and 29.64% higher NP values analyzed with the existing for Classifying AD Stages through Brain Modifications using FBPINNs Optimized with sea\u2010horse optimizer. Then, the effectiveness of the CAD\u2010FBPINN technique is compared to other methods that are currently in use, such as AD diagnosis and classification using a convolution neural network algorithm (DC\u2010AD\u2010AlexNet), Predicting diagnosis 4\u2009years before Alzheimer's disease incident (PDP\u2010ADI\u2010GCNN), and Using the DC\u2010AD\u2010AlexNet convolution neural network algorithm, diagnose and classify AD.<\/jats:p>","DOI":"10.1002\/jemt.24727","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T07:36:41Z","timestamp":1734680201000},"page":"1115-1127","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Classifying Alzheimer's Disease Using a Finite Basis Physics\u00a0Neural Network"],"prefix":"10.1002","volume":"88","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5829-4572","authenticated-orcid":false,"given":"Logeshwari","family":"Dhavamani","sequence":"first","affiliation":[{"name":"Department of Information Technology St Joseph's Institute of Technology  Chennai Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5513-6616","authenticated-orcid":false,"given":"Sagar Vasantrao","family":"Joshi","sequence":"additional","affiliation":[{"name":"Department of Electronics and Telecommunication Engineering Nutan Maharashtra Institute of Engineering and Technology  Pune Maharashtra India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8497-0626","authenticated-orcid":false,"given":"Pavan Kumar Varma","family":"Kothapalli","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Sagi Rama KrishnamRaju Engineering College  Bhimavaram Andhra Pradesh India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2349-3701","authenticated-orcid":false,"given":"Muniyandy","family":"Elangovan","sequence":"additional","affiliation":[{"name":"Department of Biosciences, Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences  Chennai Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5236-4904","authenticated-orcid":false,"given":"Ramesh Babu","family":"Putchanuthala","sequence":"additional","affiliation":[{"name":"Associate Professor, Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation  Hyderabad India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6500-5255","authenticated-orcid":false,"given":"Ramasamy","family":"Senthamil Selvan","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering Annamacharaya Institute of Technology and Sciences  Tirupati Andhra Pradesh India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,12,20]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dscb.2023.100110"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-022-06762-0"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-15738-7"},{"key":"e_1_2_8_5_1","first-page":"262","article-title":"Retinal Imaging and Alzheimer's Disease: A Future Powered by Artificial Intelligence","volume":"1","author":"Ashayeri H.","year":"2024","journal-title":"Graefe's Archive for Clinical and Experimental 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