Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia
Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be d...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/3/778 |
| _version_ | 1797407858319425536 |
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| author | Nitsa J. Herzog George D. Magoulas |
| author_facet | Nitsa J. Herzog George D. Magoulas |
| author_sort | Nitsa J. Herzog |
| collection | DOAJ |
| description | Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer’s Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries. |
| first_indexed | 2024-03-09T03:47:44Z |
| format | Article |
| id | doaj.art-bcbc9057b96a49b898862a82557c6cec |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T03:47:44Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bcbc9057b96a49b898862a82557c6cec2023-12-03T14:31:49ZengMDPI AGSensors1424-82202021-01-0121377810.3390/s21030778Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early DementiaNitsa J. Herzog0George D. Magoulas1Department of Computer Science, Birkbeck College, University of London, London WC1E 7HZ, UKDepartment of Computer Science, Birkbeck College, University of London, London WC1E 7HZ, UKEarly identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer’s Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.https://www.mdpi.com/1424-8220/21/3/778asymmetry detectionbrain asymmetrybrain MRIdementiamachine learning methodsSVM |
| spellingShingle | Nitsa J. Herzog George D. Magoulas Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia Sensors asymmetry detection brain asymmetry brain MRI dementia machine learning methods SVM |
| title | Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia |
| title_full | Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia |
| title_fullStr | Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia |
| title_full_unstemmed | Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia |
| title_short | Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia |
| title_sort | brain asymmetry detection and machine learning classification for diagnosis of early dementia |
| topic | asymmetry detection brain asymmetry brain MRI dementia machine learning methods SVM |
| url | https://www.mdpi.com/1424-8220/21/3/778 |
| work_keys_str_mv | AT nitsajherzog brainasymmetrydetectionandmachinelearningclassificationfordiagnosisofearlydementia AT georgedmagoulas brainasymmetrydetectionandmachinelearningclassificationfordiagnosisofearlydementia |