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An improved deep learning unsupervised approach for MRI tissue segmentation for Alzheimer's Disease Detection
   

An improved deep learning unsupervised approach for MRI tissue segmentation for Alzheimer's Disease Detection

Karan Kumar, Isha Suwalka, Adaora Uche Ezennia, Celestine Iwendi Cresantus Biamba
IEEE Access, Vol.12, pp.188114-188121
19/12/2024
: https://doi.org/10.1109/ACCESS.2024.3510454
 
adaptive movong mapping Alzheimer's disease Clustering Feature extraction OASIS Medical Technology
Alzheimer's disease (AD) ranks as the sixth leading cause of death, emphasizing the need for early-stage prediction to prevent its progression. Due to the complexity and heterogeneity of medical tests, manually comparing, visualizing, and analyzing data is often difficult and time-consuming. As a result, a computational approach for accurately predicting brain changes through the classification of magnetic resonance imaging (MRI) scans becomes highly valuable, though challenging. This paper introduces a novel method for diagnosing the early stages of AD by utilizing an efficient mapping technique to differentiate between affected and normal MRI scans. The approach combines a hybrid unsupervised learning framework, specifically the adaptive moving self-organizing map (AMSOM) method integrated with Fuzzy K-means. To ensure optimal feature extraction, we introduce a hybrid learning framework that embeds feature vectors in a subspace. The analysis compares various mapping approaches to identify features linked to Alzheimer’s disease. The proposed method achieves a classification accuracy of 95.75% on the Open Access Series of Imaging Studies (OASIS) MRI brain image database, outperforming existing methods.

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An Improved Deep Learning Unsupervised Approach for MRI Tissue Segmentation for Alzheimer’s Disease Detection4.09 MB
Published (Version of record) Open Access  — You are free to: Share — copy and redistribute the material in any medium or format for any purpose, even commercially. Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
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An improved deep learning unsupervised approach for MRI tissue segmentation for Alzheimer’s Disease Detection
Published (Version of record)  — You are free to: Share — copy and redistribute the material in any medium or format for any purpose, even commercially. Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
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