Early Diagnosis of Alzheimer’s Disease Using Adaptive Neuro K-Means Clustering Technique
Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by memory loss, behavioral changes, and impaired self-care, often preceded by Mild Cognitive Impairment (MCI). Not all MCI cases progress to AD, creating a diagnostic challenge. This study proposes a novel...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10852308/ |
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| author | Karan Kumar Shweta Agrawal Isha Suwalka Celestine Iwendi Cresantus N. Biamba |
| author_facet | Karan Kumar Shweta Agrawal Isha Suwalka Celestine Iwendi Cresantus N. Biamba |
| author_sort | Karan Kumar |
| collection | DOAJ |
| description | Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by memory loss, behavioral changes, and impaired self-care, often preceded by Mild Cognitive Impairment (MCI). Not all MCI cases progress to AD, creating a diagnostic challenge. This study proposes a novel framework for early AD diagnosis using T1-weighted Magnetic Resonance Imaging (MRI). The approach integrates the Adaptive Moving Self-Organizing Map (AMSOM), a neural network technique for unsupervised training and tissue segmentation, with K-means clustering and Principal Component Analysis (PCA) for feature selection. AMSOM dynamically updates neuron weights to improve segmentation accuracy. Classification is performed using various algorithms, evaluated on sensitivity, accuracy, precision, and similarity metrics. Compared to existing techniques such as Fuzzy C-means (FCM) and hybrid Self-Organizing Mapping-K-means (SOM-FKM), the proposed method demonstrates statistically significant improvements in tissue segmentation and classification. It achieved a mean accuracy of 99.8%, reducing the Mean Squared Error (MSE) from 2.3 to 0.44 and improving the Discriminative Overlap Index (DOI) and Tissue Clarity (TC) values to 0.435105 and 0.282381, respectively. Implemented in MATLAB, this method provides a robust, efficient framework for early AD detection, surpassing existing approaches in precision and reliability. |
| format | Article |
| id | doaj-art-493b11d57a7345a8859a16e27bab49c7 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-493b11d57a7345a8859a16e27bab49c72025-02-07T00:01:22ZengIEEEIEEE Access2169-35362025-01-0113227742278310.1109/ACCESS.2025.353363810852308Early Diagnosis of Alzheimer’s Disease Using Adaptive Neuro K-Means Clustering TechniqueKaran Kumar0https://orcid.org/0000-0002-9038-0099Shweta Agrawal1https://orcid.org/0000-0002-0372-6128Isha Suwalka2Celestine Iwendi3https://orcid.org/0000-0003-4350-3911Cresantus N. Biamba4https://orcid.org/0000-0002-0589-7924Electronics and Communication Engineering Department, Maharishi Markandeshwar Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, IndiaDepartment of CSE-AIML, Indore Institute of Science and Technology, Indore, IndiaIndira IVF Hospital Pvt. Ltd., Udaipur, Rajasthan, IndiaSchool of Creative Technologies, University of Bolton, Bolton, U.K.Department of Educational Sciences, University of Gävle, Gävle, SwedenAlzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by memory loss, behavioral changes, and impaired self-care, often preceded by Mild Cognitive Impairment (MCI). Not all MCI cases progress to AD, creating a diagnostic challenge. This study proposes a novel framework for early AD diagnosis using T1-weighted Magnetic Resonance Imaging (MRI). The approach integrates the Adaptive Moving Self-Organizing Map (AMSOM), a neural network technique for unsupervised training and tissue segmentation, with K-means clustering and Principal Component Analysis (PCA) for feature selection. AMSOM dynamically updates neuron weights to improve segmentation accuracy. Classification is performed using various algorithms, evaluated on sensitivity, accuracy, precision, and similarity metrics. Compared to existing techniques such as Fuzzy C-means (FCM) and hybrid Self-Organizing Mapping-K-means (SOM-FKM), the proposed method demonstrates statistically significant improvements in tissue segmentation and classification. It achieved a mean accuracy of 99.8%, reducing the Mean Squared Error (MSE) from 2.3 to 0.44 and improving the Discriminative Overlap Index (DOI) and Tissue Clarity (TC) values to 0.435105 and 0.282381, respectively. Implemented in MATLAB, this method provides a robust, efficient framework for early AD detection, surpassing existing approaches in precision and reliability.https://ieeexplore.ieee.org/document/10852308/AMSOMclassificationimage segmentationMRIPCAGLCM |
| spellingShingle | Karan Kumar Shweta Agrawal Isha Suwalka Celestine Iwendi Cresantus N. Biamba Early Diagnosis of Alzheimer’s Disease Using Adaptive Neuro K-Means Clustering Technique IEEE Access AMSOM classification image segmentation MRI PCA GLCM |
| title | Early Diagnosis of Alzheimer’s Disease Using Adaptive Neuro K-Means Clustering Technique |
| title_full | Early Diagnosis of Alzheimer’s Disease Using Adaptive Neuro K-Means Clustering Technique |
| title_fullStr | Early Diagnosis of Alzheimer’s Disease Using Adaptive Neuro K-Means Clustering Technique |
| title_full_unstemmed | Early Diagnosis of Alzheimer’s Disease Using Adaptive Neuro K-Means Clustering Technique |
| title_short | Early Diagnosis of Alzheimer’s Disease Using Adaptive Neuro K-Means Clustering Technique |
| title_sort | early diagnosis of alzheimer x2019 s disease using adaptive neuro k means clustering technique |
| topic | AMSOM classification image segmentation MRI PCA GLCM |
| url | https://ieeexplore.ieee.org/document/10852308/ |
| work_keys_str_mv | AT karankumar earlydiagnosisofalzheimerx2019sdiseaseusingadaptiveneurokmeansclusteringtechnique AT shwetaagrawal earlydiagnosisofalzheimerx2019sdiseaseusingadaptiveneurokmeansclusteringtechnique AT ishasuwalka earlydiagnosisofalzheimerx2019sdiseaseusingadaptiveneurokmeansclusteringtechnique AT celestineiwendi earlydiagnosisofalzheimerx2019sdiseaseusingadaptiveneurokmeansclusteringtechnique AT cresantusnbiamba earlydiagnosisofalzheimerx2019sdiseaseusingadaptiveneurokmeansclusteringtechnique |