Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer’s Disease
Alzheimer’s disease is a common neurological disorder and mental disability that causes memory loss and cognitive decline, presenting a major challenge to public health due to its impact on millions of individuals worldwide. It is crucial to diagnose and treat Alzheimer’s in a timely manner to impro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/11/20/2763 |
| _version_ | 1797573690997604352 |
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| author | Maram Fahaad Almufareh Samabia Tehsin Mamoona Humayun Sumaira Kausar |
| author_facet | Maram Fahaad Almufareh Samabia Tehsin Mamoona Humayun Sumaira Kausar |
| author_sort | Maram Fahaad Almufareh |
| collection | DOAJ |
| description | Alzheimer’s disease is a common neurological disorder and mental disability that causes memory loss and cognitive decline, presenting a major challenge to public health due to its impact on millions of individuals worldwide. It is crucial to diagnose and treat Alzheimer’s in a timely manner to improve the quality of life of both patients and caregivers. In the recent past, machine learning techniques have showed potential in detecting Alzheimer’s disease by examining neuroimaging data, especially Magnetic Resonance Imaging (MRI). This research proposes an attention-based mechanism that employs the vision transformer approach to detect Alzheimer’s using MRI images. The presented technique applies preprocessing to the MRI images and forwards them to a vision transformer network for classification. This network is trained on the publicly available Kaggle dataset, and it illustrated impressive results with an accuracy of 99.06%, precision of 99.06%, recall of 99.14%, and F1-score of 99.1%. Furthermore, a comparative study is also conducted to evaluate the performance of the proposed method against various state-of-the-art techniques on diverse datasets. The proposed method demonstrated superior performance, outperforming other published methods when applied to the Kaggle dataset. |
| first_indexed | 2024-03-10T21:13:35Z |
| format | Article |
| id | doaj.art-41a7d0ecb1d647ecaa6e1363b68c3f1a |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-10T21:13:35Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-41a7d0ecb1d647ecaa6e1363b68c3f1a2023-11-19T16:37:46ZengMDPI AGHealthcare2227-90322023-10-011120276310.3390/healthcare11202763Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer’s DiseaseMaram Fahaad Almufareh0Samabia Tehsin1Mamoona Humayun2Sumaira Kausar3Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah 72388, Saudi ArabiaDepartment of Computer Science, Bahria University, Islamabad 44000, PakistanDepartment of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah 72388, Saudi ArabiaDepartment of Computer Science, Bahria University, Islamabad 44000, PakistanAlzheimer’s disease is a common neurological disorder and mental disability that causes memory loss and cognitive decline, presenting a major challenge to public health due to its impact on millions of individuals worldwide. It is crucial to diagnose and treat Alzheimer’s in a timely manner to improve the quality of life of both patients and caregivers. In the recent past, machine learning techniques have showed potential in detecting Alzheimer’s disease by examining neuroimaging data, especially Magnetic Resonance Imaging (MRI). This research proposes an attention-based mechanism that employs the vision transformer approach to detect Alzheimer’s using MRI images. The presented technique applies preprocessing to the MRI images and forwards them to a vision transformer network for classification. This network is trained on the publicly available Kaggle dataset, and it illustrated impressive results with an accuracy of 99.06%, precision of 99.06%, recall of 99.14%, and F1-score of 99.1%. Furthermore, a comparative study is also conducted to evaluate the performance of the proposed method against various state-of-the-art techniques on diverse datasets. The proposed method demonstrated superior performance, outperforming other published methods when applied to the Kaggle dataset.https://www.mdpi.com/2227-9032/11/20/2763mental disabilitydiagnosisAlzheimer’s diseasemedical image analysisvision transformer |
| spellingShingle | Maram Fahaad Almufareh Samabia Tehsin Mamoona Humayun Sumaira Kausar Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer’s Disease Healthcare mental disability diagnosis Alzheimer’s disease medical image analysis vision transformer |
| title | Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer’s Disease |
| title_full | Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer’s Disease |
| title_fullStr | Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer’s Disease |
| title_full_unstemmed | Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer’s Disease |
| title_short | Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer’s Disease |
| title_sort | artificial cognition for detection of mental disability a vision transformer approach for alzheimer s disease |
| topic | mental disability diagnosis Alzheimer’s disease medical image analysis vision transformer |
| url | https://www.mdpi.com/2227-9032/11/20/2763 |
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