Improving early detection of Alzheimer’s disease through MRI slice selection and deep learning techniques
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 EMC...
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Nature Portfolio
2025-08-01
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| Online Access: | https://doi.org/10.1038/s41598-025-14476-0 |
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| author | Begüm Şener Koray Açıcı Emre Sümer |
| author_facet | Begüm Şener Koray Açıcı Emre Sümer |
| author_sort | Begüm Şener |
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| description | 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. |
| format | Article |
| id | doaj-art-5632bc1201a746f5841a2d75431e45da |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-5632bc1201a746f5841a2d75431e45da2025-08-20T04:02:56ZengNature PortfolioScientific Reports2045-23222025-08-0115112210.1038/s41598-025-14476-0Improving early detection of Alzheimer’s disease through MRI slice selection and deep learning techniquesBegüm Şener0Koray Açıcı1Emre Sümer2Department of Computer Engineering, Başkent UniversityDepartment of Artificial Intelligence and Data Engineering, Ankara UniversityDepartment of Computer Engineering, Başkent UniversityAbstract 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.https://doi.org/10.1038/s41598-025-14476-0Alzheimer’s diseaseSlice selectionDeep learningVision transformersEarly Mild Cognitive Impairment |
| spellingShingle | Begüm Şener Koray Açıcı Emre Sümer Improving early detection of Alzheimer’s disease through MRI slice selection and deep learning techniques Scientific Reports Alzheimer’s disease Slice selection Deep learning Vision transformers Early Mild Cognitive Impairment |
| title | Improving early detection of Alzheimer’s disease through MRI slice selection and deep learning techniques |
| title_full | Improving early detection of Alzheimer’s disease through MRI slice selection and deep learning techniques |
| title_fullStr | Improving early detection of Alzheimer’s disease through MRI slice selection and deep learning techniques |
| title_full_unstemmed | Improving early detection of Alzheimer’s disease through MRI slice selection and deep learning techniques |
| title_short | Improving early detection of Alzheimer’s disease through MRI slice selection and deep learning techniques |
| title_sort | improving early detection of alzheimer s disease through mri slice selection and deep learning techniques |
| topic | Alzheimer’s disease Slice selection Deep learning Vision transformers Early Mild Cognitive Impairment |
| url | https://doi.org/10.1038/s41598-025-14476-0 |
| work_keys_str_mv | AT begumsener improvingearlydetectionofalzheimersdiseasethroughmrisliceselectionanddeeplearningtechniques AT korayacıcı improvingearlydetectionofalzheimersdiseasethroughmrisliceselectionanddeeplearningtechniques AT emresumer improvingearlydetectionofalzheimersdiseasethroughmrisliceselectionanddeeplearningtechniques |