{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:06:43Z","timestamp":1791522403491,"version":"4.3.4"},"reference-count":29,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAD"],"published-print":{"date-parts":[[2023,5,2]]},"abstract":"<jats:p>Background: Alzheimer\u2019s disease (AD) is a neurodegenerative disease that drastically affects brain cells. Early detection of this disease can reduce the brain cell damage rate and improve the prognosis of the patient to a great extent. The patients affected with AD tend to depend on their children and relatives for their daily chores. Objective: This research study utilizes the latest technologies of artificial intelligence and computation power to aid the medical industry. The study aims at early detection of AD to enable doctors to treat patients with the appropriate medication in the early stages of the disease condition. Methods: In this research study, convolutional neural networks, an advanced deep learning technique, are adopted to classify AD patients with their MRI images. Deep learning models with customized architecture are precise in the early detection of diseases with images retrieved by neuroimaging techniques. Results: The convolution neural network model classifies the patients as diagnosed with AD or cognitively normal. Standard metrics evaluate the model performance to compare with the state-of-the-art methodologies. The experimental study of the proposed model shows promising results with an accuracy of 97%, precision of 94%, recall rate of 94%, and f1-score of 94%. Conclusion: This study leverages powerful technologies like deep learning to aid medical practitioners in diagnosing AD. It is crucial to detect AD early to control and slow down the rate at which the disease progresses.<\/jats:p>","DOI":"10.3233\/jad-221250","type":"journal-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T12:36:02Z","timestamp":1679661362000},"page":"235-245","source":"Crossref","is-referenced-by-count":9,"title":["Artificial Intelligence Model for Alzheimer\u2019s Disease Detection with Convolution Neural Network for Magnetic Resonance Images"],"prefix":"10.1177","volume":"93","author":[{"given":"Shabana R.","family":"Ziyad","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meshal","family":"Alharbi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"May","family":"Altulyan","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/JAD-221250_ref1","doi-asserted-by":"crossref","first-page":"217830","DOI":"10.1109\/ACCESS.2020.3040486","article-title":"3D CNN design for the classification of Alzheimer\u2019s disease using brain MRI and PET","volume":"8","author":"Khagi","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/JAD-221250_ref5","first-page":"123","article-title":"Alzheimer\u2019s disease \u2013why we need early diagnosis","volume":"9","author":"Rasmussen","year":"2019","journal-title":"Degener Neurol Neuromuscul Dis"},{"key":"10.3233\/JAD-221250_ref7","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.zemedi.2018.11.002","article-title":"An overview of deep learning in medical imaging focusing on MRI","volume":"29","author":"Lundervold","year":"2019","journal-title":"Z Med Phys"},{"key":"10.3233\/JAD-221250_ref8","doi-asserted-by":"crossref","first-page":"44","DOI":"10.3390\/app12010044","article-title":"Ensemble-guided model for performance enhancement in model-complexity-limited acoustic scene classification","volume":"12","author":"Lee","year":"2022","journal-title":"Appl Sci"},{"key":"10.3233\/JAD-221250_ref9","doi-asserted-by":"crossref","first-page":"1890","DOI":"10.3390\/app11041890","article-title":"A study on a speech emotion recognition system with effective acoustic features using deep learning algorithms","volume":"11","author":"Byun","year":"2021","journal-title":"Appl Sci"},{"key":"10.3233\/JAD-221250_ref10","first-page":"e6028693","article-title":"Natural language processing with improved deep learning neural networks","volume":"2022","author":"Zhou","year":"2022","journal-title":"Sci Program"},{"key":"10.3233\/JAD-221250_ref11","doi-asserted-by":"crossref","unstructured":"Fantin Irudaya Raj E , Balaji M (2022) Application of deep learning and machine learning in pattern recognition. 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