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<p>Dementia can occur in various forms; some types of dementia are curable and some are not. Among the non-curable forms, Alzheimer’s disease (AD) is the prominent one. There is no effective treatment to cure it but it may reduce its impact if diagnosed early. Accurate classification of AD is necessary for diagnosing and giving the right effective treatment to patients. Specific regions in the brain serve as hotspots for AD, potential imaging biomarkers that contribute effectively thereby improving classification accuracy. We introduce a new Attention-based ’U’ shaped Convolution Neural Network in this study to identify imaging biomarkers of AD using 3D T1-weighted MRI data, which excels at distinguishing between gray matter, white matter, and cerebrospinal fluid (CSF). Our model is improved UNet with an enhanced convolution block attention module named EnCBAMUNet. This makes it ideal for brain imaging, where the differentiation between these tissues is crucial for identifying pathology, such as in neurodegenerative diseases. Our method is tested on both ADNI and OASIS datasets. Classification accuracy of 99.8% for Healthy as control normal(CN) vs Alzheimer’s disease(AD) is obtained in binary classification and 95.5% for multi-classifier Alzheimer’s Disease, Mild Cognitive Impairment(MCI) is a transient state from healthy to disease state and Control Normal as Healthy(AD vs MCI vs CN) in multiclass classification. We visualize regions (hippocampus, ventricles, and some parts of cortex) of the brain responsible for AD using the Three Dimensional Gradient-weighted Class Activation Mapping (3D-Grad-CAM) method of our model due to the deep learning model(DL) that is black-box in nature.</p>

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MRI-Based Biomarker in the Diagnosis of Alzheimer’s Disease Using Attention-UNet

  • 期刊论文

  • 发表日期：

    2025年2月21日

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  • J. Arumugam,
  • V. Prasanna Venkatesan,
  • Tabiya Beigh

摘要

Dementia can occur in various forms; some types of dementia are curable and some are not. Among the non-curable forms, Alzheimer’s disease (AD) is the prominent one. There is no effective treatment to cure it but it may reduce its impact if diagnosed early. Accurate classification of AD is necessary for diagnosing and giving the right effective treatment to patients. Specific regions in the brain serve as hotspots for AD, potential imaging biomarkers that contribute effectively thereby improving classification accuracy. We introduce a new Attention-based ’U’ shaped Convolution Neural Network in this study to identify imaging biomarkers of AD using 3D T1-weighted MRI data, which excels at distinguishing between gray matter, white matter, and cerebrospinal fluid (CSF). Our model is improved UNet with an enhanced convolution block attention module named EnCBAMUNet. This makes it ideal for brain imaging, where the differentiation between these tissues is crucial for identifying pathology, such as in neurodegenerative diseases. Our method is tested on both ADNI and OASIS datasets. Classification accuracy of 99.8% for Healthy as control normal(CN) vs Alzheimer’s disease(AD) is obtained in binary classification and 95.5% for multi-classifier Alzheimer’s Disease, Mild Cognitive Impairment(MCI) is a transient state from healthy to disease state and Control Normal as Healthy(AD vs MCI vs CN) in multiclass classification. We visualize regions (hippocampus, ventricles, and some parts of cortex) of the brain responsible for AD using the Three Dimensional Gradient-weighted Class Activation Mapping (3D-Grad-CAM) method of our model due to the deep learning model(DL) that is black-box in nature.