Leveraging transformers and explainable AI for Alzheimer's disease interpretability.
Alzheimer's disease (AD) is a progressive brain ailment that causes memory loss, cognitive decline, and behavioral changes. It is quite concerning that one in nine adults over the age of 65 have AD. Currently there is almost no cure for AD except very few experimental treatments. However, early...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0322607 |
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| author | Humaira Anzum Nabil Sadd Sammo Shamim Akhter |
| author_facet | Humaira Anzum Nabil Sadd Sammo Shamim Akhter |
| author_sort | Humaira Anzum |
| collection | DOAJ |
| description | Alzheimer's disease (AD) is a progressive brain ailment that causes memory loss, cognitive decline, and behavioral changes. It is quite concerning that one in nine adults over the age of 65 have AD. Currently there is almost no cure for AD except very few experimental treatments. However, early detection offers chances to take part in clinical trials or other investigations looking at potential new and effective Alzheimer's treatments. To detect Alzheimer's disease, brain scans such as computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) can be performed. Many researches have been undertaken to use computer vision on MRI images, and their accuracy ranges from 80-90%, new computer vision algorithms and cutting-edge transformers have the potential to improve this performance.We utilize advanced transformers and computer vision algorithms to enhance diagnostic accuracy, achieving an impressive 99% accuracy in categorizing Alzheimer's disease stages through translating RNA text data and brain MRI images in near-real-time. We integrate the Local Interpretable Model-agnostic Explanations (LIME) explainable AI (XAI) technique to ensure the transformers' acceptance, reliability, and human interpretability. LIME helps identify crucial features in RNA sequences or specific areas in MRI images essential for diagnosing AD. |
| format | Article |
| id | doaj-art-8fa0882ddc714071a94bfca0512a50fa |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-8fa0882ddc714071a94bfca0512a50fa2025-08-20T03:08:27ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01205e032260710.1371/journal.pone.0322607Leveraging transformers and explainable AI for Alzheimer's disease interpretability.Humaira AnzumNabil Sadd SammoShamim AkhterAlzheimer's disease (AD) is a progressive brain ailment that causes memory loss, cognitive decline, and behavioral changes. It is quite concerning that one in nine adults over the age of 65 have AD. Currently there is almost no cure for AD except very few experimental treatments. However, early detection offers chances to take part in clinical trials or other investigations looking at potential new and effective Alzheimer's treatments. To detect Alzheimer's disease, brain scans such as computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) can be performed. Many researches have been undertaken to use computer vision on MRI images, and their accuracy ranges from 80-90%, new computer vision algorithms and cutting-edge transformers have the potential to improve this performance.We utilize advanced transformers and computer vision algorithms to enhance diagnostic accuracy, achieving an impressive 99% accuracy in categorizing Alzheimer's disease stages through translating RNA text data and brain MRI images in near-real-time. We integrate the Local Interpretable Model-agnostic Explanations (LIME) explainable AI (XAI) technique to ensure the transformers' acceptance, reliability, and human interpretability. LIME helps identify crucial features in RNA sequences or specific areas in MRI images essential for diagnosing AD.https://doi.org/10.1371/journal.pone.0322607 |
| spellingShingle | Humaira Anzum Nabil Sadd Sammo Shamim Akhter Leveraging transformers and explainable AI for Alzheimer's disease interpretability. PLoS ONE |
| title | Leveraging transformers and explainable AI for Alzheimer's disease interpretability. |
| title_full | Leveraging transformers and explainable AI for Alzheimer's disease interpretability. |
| title_fullStr | Leveraging transformers and explainable AI for Alzheimer's disease interpretability. |
| title_full_unstemmed | Leveraging transformers and explainable AI for Alzheimer's disease interpretability. |
| title_short | Leveraging transformers and explainable AI for Alzheimer's disease interpretability. |
| title_sort | leveraging transformers and explainable ai for alzheimer s disease interpretability |
| url | https://doi.org/10.1371/journal.pone.0322607 |
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