Leveraging transformers and explainable AI for Alzheimer’s disease interpretability

• Published in: PloS one Vol. 20; no. 5; p. e0322607
• Main Authors: Anzum, Humaira, Sammo, Nabil Sadd, Akhter, Shamim
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23.05.2025; Public Library of Science (PLoS)
• Subjects: Accuracy; Aged; Algorithms; Alzheimer Disease - diagnosis; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Artificial Intelligence; Brain; Brain - diagnostic imaging; Brain - pathology; Clinical trials; Cognitive ability; Computed tomography; Computer vision; Datasets; Diagnosis; Explainable artificial intelligence; Female; Gene sequencing; Health aspects; Humans; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Neurodegenerative diseases; Neuroimaging; Positron emission; Positron emission tomography; Real time; Support vector machines; Bangladesh
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0322607

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.