{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T16:27:10Z","timestamp":1792513630209,"version":"4.5.0"},"reference-count":41,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T00:00:00Z","timestamp":1685491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>In the present scenario, Alzheimer\u2019s Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging modalities, functional Magnetic Resonance Imaging (fMRI) is extensively utilized for studying brain activities related to AD. However, analyzing complex brain structures in fMRI is a time-consuming and complex task; so, a novel automated model was proposed in this manuscript for early diagnosis of AD using fMRI images. Initially, the fMRI images are acquired from an online dataset: Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI). Further, the quality of the acquired fMRI images was improved by implementing a normalization technique. Then, the Segmentation by Aggregating Superpixels (SAS) method was implemented for segmenting the brain regions (AD, Normal Controls (NC), Mild Cognitive Impairment (MCI), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Significant Memory Concern (SMC)) from the denoised fMRI images. From the segmented brain regions, feature vectors were extracted by employing Gabor and Gray Level Co-Occurrence Matrix (GLCM) techniques. The obtained feature vectors were dimensionally reduced by implementing Honey Badger Optimization Algorithm (HBOA) and fed to the Multi-Layer Perceptron (MLP) model for classifying the fMRI images as AD, NC, MCI, EMCI, LMCI, and SMC. The extensive investigation indicated that the presented model attained 99.44% of classification accuracy, 88.90% of Dice Similarity Coefficient (DSC), 90.82% of Jaccard Coefficient (JC), and 88.43% of Hausdorff Distance (HD). The attained results are better compared with the conventional segmentation and classification models.<\/jats:p>","DOI":"10.3390\/brainsci13060893","type":"journal-article","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T03:06:00Z","timestamp":1685588760000},"page":"893","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["fMRI-Based Alzheimer\u2019s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network"],"prefix":"10.3390","volume":"13","author":[{"given":"Aarthi","family":"Chelladurai","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, Sengunthar Engineering College, Tiruchengode 637205, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dayanand Lal","family":"Narayan","sequence":"additional","affiliation":[{"name":"Department of Computer Science Engineering, GITAM School of Technology, GITAM University, Bengaluru 561203, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3997-5070","authenticated-orcid":false,"given":"Parameshachari Bidare","family":"Divakarachari","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bengaluru 560064, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umasankar","family":"Loganathan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, S.A. Engineering College, Chennai 600077, Tamilnadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e01407","DOI":"10.1002\/brb3.1407","article-title":"Functional connectivity between white matter and gray matter based on fMRI for Alzheimer\u2019s disease classification","volume":"9","author":"Zhao","year":"2019","journal-title":"Brain Behav."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sarraf, S., Sarraf, A., DeSouza, D.D., Anderson, J.A.E., Kabia, M., and Alzheimer\u2019s Disease Neuroimaging Initiative (2023). OViTAD: Optimized vision transformer to predict various stages of Alzheimer\u2019s disease using resting-state fMRI and structural MRI data. 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