{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T11:42:39Z","timestamp":1770464559712,"version":"3.49.0"},"reference-count":35,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2024,6,7]]},"abstract":"<jats:p>Alzheimer\u2019s disease (AD) is a neurodegenerative disorder that affects millions of individuals worldwide, causing progressive cognitive decline. Early prediction and diagnosis the AD accurately is crucial for effective intervention and treatment. In this study, we propose a comprehensive framework for AD prediction using various techniques, including preprocessing and denoising with Multilayer Perceptron (MLP) and Ant Colony Optimization (ACO), segmentation using U-Net, and classification with Spatial Pyramid Pooling Network (SPPNet). Furthermore, we employ Convolutional Neural Network (CNN) with SPPNet for training and develop a chatbot for recommendation based on MRI data input. The preprocessing and denoising techniques play a vital role in enhancing the quality of the input data. MLP is utilized for preprocessing, where it effectively handles feature extraction and noise reduction. ACO is employed for denoising, optimizing the data to improve the signal-to-noise ratio, and enhancing the overall performance of subsequent stages. For accurate segmentation of brain regions, we employ the U-Net architecture, which has shown remarkable success in medical image segmentation tasks. U-Net effectively identifies the regions of interest, aiding in subsequent classification stages. The classification phase utilizes SPPNet, a deep learning model known for its ability to capture spatial information at multiple scales. SPPNet extracts features from segmented brain regions, enabling robust classification of AD and non-AD cases. To enhance the training process, we employ CNN with SPPNet, leveraging the power of convolutional layers to capture intricate patterns and improve predictive accuracy. The CNN-SPPNet model is trained on a large dataset of MRI scans, enabling it to learn complex representations and make accurate predictions. Hence the proposed work can be integrated with a chatbot that takes MRI data as input and provides recommendations based on the predicted AD probability. Experimental evaluation shows that the combination of preprocessing, denoising, segmentation, and classification offers a comprehensive solution for accurate and efficient AD diagnosis and management.<\/jats:p>","DOI":"10.3233\/idt-230635","type":"journal-article","created":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T15:34:43Z","timestamp":1707492883000},"page":"1541-1556","source":"Crossref","is-referenced-by-count":4,"title":["An effectual recommendation model using hybrid learning models for early detection of Alzheimer\u2019s disease"],"prefix":"10.1177","volume":"18","author":[{"given":"V.","family":"Sanjay","sequence":"first","affiliation":[]},{"given":"P.","family":"Swarnalatha","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"issue":"12","key":"10.3233\/IDT-230635_ref1","doi-asserted-by":"crossref","first-page":"1902","DOI":"10.1016\/j.neurobiolaging.2008.01.016","article-title":"Clinical severity of Alzheimer\u2019s disease is associated with PIB uptake in PET","volume":"30","author":"Grimmer","year":"2009","journal-title":"Neurobiology of Aging."},{"issue":"9","key":"10.3233\/IDT-230635_ref2","doi-asserted-by":"crossref","first-page":"1654","DOI":"10.3390\/diagnostics13091654","article-title":"Automatic Analysis of MRI Images for Early Prediction of Alzheimer\u2019s Disease Stages Based on Hybrid Features of CNN and Handcrafted Features","volume":"13","author":"Khalid","year":"2023","journal-title":"Diagnostics."},{"issue":"4","key":"10.3233\/IDT-230635_ref3","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/TMI.2007.908685","article-title":"MRI-based automated computer classification of probable AD versus normal controls","volume":"27","author":"Duchesne","year":"2008","journal-title":"IEEE Transactions on Medical Imaging."},{"issue":"1","key":"10.3233\/IDT-230635_ref4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12877-016-0298-y","article-title":"Impact of caring for persons with Alzheimer\u2019s disease or dementia on caregivers\u2019 health outcomes: findings from a community based survey in Japan","volume":"16","author":"Goren","year":"2016","journal-title":"BMC geriatrics."},{"key":"10.3233\/IDT-230635_ref5","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.trsl.2022.12.003","article-title":"Vascular contributions to Alzheimer\u2019s disease","volume":"254","author":"Eisenmenger","year":"2023","journal-title":"Translational Research."},{"key":"10.3233\/IDT-230635_ref6","doi-asserted-by":"crossref","unstructured":"Piller C. 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