Improving early detection of Alzheimer's disease through MRI slice selection and deep learning techniques
Begüm Şener1, Koray Açıcı2, Emre Sümer3
1Department of Computer Engineering, Başkent University, Ankara, Turkey. begume@baskent.edu.tr.
Abstract:
Alzheimer's disease is a progressive neurodegenerative disorder marked by cognitive decline, memory loss, and behavioral changes. Early diagnosis, particularly identifying Early Mild Cognitive Impairment (EMCI), is vital for managing the disease and improving patient outcomes. Detecting EMCI is challenging due to the subtle structural changes in the brain, making precise slice selection from MRI scans essential for accurate diagnosis. In this context, the careful selection of specific MRI slices that provide distinct anatomical details significantly enhances the ability to identify these early changes. The chief novelty of the study is that instead of selecting all slices, an approach for identifying the important slices is developed. The ADNI-3 dataset was used as the dataset when running the models for early detection of Alzheimer's disease. Satisfactory results have been obtained by classifying with deep learning models, vision transformers (ViT) and by adding new structures to them, together with the model proposal. In the results obtained, while an accuracy of 99.45% was achieved with EfficientNetB2 + FPN in AD vs. LMCI classification from the slices selected with SSIM, an accuracy of 99.19% was achieved in AD vs. EMCI classification, in fact, the study significantly advances early detection by demonstrating improved diagnostic accuracy of the disease at the EMCI stage. The results obtained with these methods emphasize the importance of developing deep learning models with slice selection integrated with the Vision Transformers architecture. Focusing on accurate slice selection enables early detection of Alzheimer's at the EMCI stage, allowing for timely interventions and preventive measures before the disease progresses to more advanced stages. This approach not only facilitates early and accurate diagnosis, but also lays the groundwork for timely intervention and treatment, offering hope for better patient outcomes in Alzheimer's disease. The study is finally evaluated by a statistical significance test.
Insights
This study introduces a novel slice selection method for Alzheimer's disease (AD) detection using MRI scans. It improves early diagnosis of Mild Cognitive Impairment (MCI) by focusing on key brain slices, enhancing patient outcomes.
Area of Science:
- Neuroimaging
- Artificial Intelligence
- Neurology
Background:
- Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline.
- Early diagnosis of Early Mild Cognitive Impairment (EMCI) is crucial for effective management and improved patient outcomes.
- Identifying subtle brain changes in EMCI from MRI scans is challenging, necessitating precise slice selection.
Purpose of the Study:
- To develop and evaluate a novel approach for identifying critical MRI slices for early Alzheimer's disease detection.
- To enhance the accuracy of diagnosing Early Mild Cognitive Impairment (EMCI) by focusing on diagnostically relevant brain regions.
- To integrate advanced deep learning models with optimized slice selection for improved diagnostic performance.
Main Methods:
- Utilized the Alzheimer's Disease Neuroimaging Initiative (ADNI-3) dataset for model training and validation.
- Developed a novel slice selection strategy to identify anatomically informative MRI slices.
- Employed deep learning models, including Vision Transformers (ViT) and EfficientNetB2+FPN, for classification tasks.
Main Results:
- Achieved high accuracy in classifying Alzheimer's disease (AD) versus Late Mild Cognitive Impairment (LMCI) (99.45%) using selected slices and EfficientNetB2+FPN.
- Demonstrated strong performance in classifying AD versus Early Mild Cognitive Impairment (EMCI) (99.19%) with the proposed slice selection and deep learning approach.
- The integrated approach of slice selection with Vision Transformers architecture significantly advanced early AD detection at the EMCI stage.
Conclusions:
- The proposed slice selection method is effective in improving the accuracy of early Alzheimer's disease detection, particularly at the EMCI stage.
- Integrating optimized slice selection with deep learning models, especially Vision Transformers, offers a promising avenue for enhanced neurodegenerative disease diagnosis.
- Timely and accurate diagnosis through advanced imaging analysis can facilitate early intervention, potentially altering disease progression and improving patient prognosis.
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