An Exemplar Pyramid Feature Extraction Based Alzheimer Disease Classification Method
<p>Dementia is a term used to describe a variety of symptoms related to cognitive impairment in which Alzheimer disease represents 60% – 70% of the cases. As of today, there is no cure for this disease and the only way to prevent any associated medical, economic, and financial impacts or losse...
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2022
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| _version_ | 1864513561049432064 |
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| author | Heba Soliman Zaina (16904787) |
| author2 | Samir Brahim Belhaouari (16855434) Tanya Stanko (16904790) Vladimir Gorovoy (16904793) |
| author2_role | author author author |
| author_facet | Heba Soliman Zaina (16904787) Samir Brahim Belhaouari (16855434) Tanya Stanko (16904790) Vladimir Gorovoy (16904793) |
| author_role | author |
| dc.creator.none.fl_str_mv | Heba Soliman Zaina (16904787) Samir Brahim Belhaouari (16855434) Tanya Stanko (16904790) Vladimir Gorovoy (16904793) |
| dc.date.none.fl_str_mv | 2022-06-15T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2022.3183185 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/An_Exemplar_Pyramid_Feature_Extraction_Based_Alzheimer_Disease_Classification_Method/24056394 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Information and computing sciences Data management and data science Feature extraction Magnetic resonance imaging Alzheimer's disease Three-dimensional displays Support vector machines Nonlinear distortion Cognitive normal (CN) Early mild cognitive impairment (EMCI) Late mild cognitive impairment (LMCI) Alzheimer disease (AD) Local binary pattern (LBP) Gray level co-occurrence matrix (GLCM) Multi-layer perceptron (MLP) Exemplar pyramid |
| dc.title.none.fl_str_mv | An Exemplar Pyramid Feature Extraction Based Alzheimer Disease Classification Method |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Dementia is a term used to describe a variety of symptoms related to cognitive impairment in which Alzheimer disease represents 60% – 70% of the cases. As of today, there is no cure for this disease and the only way to prevent any associated medical, economic, and financial impacts or losses is to detect the disease early and work closely with suspected patients to prevent any further progress. In this research, a methodology consisting of 4 modules is proposed: (1) preprocessing, exemplar pyramid along with bi-linear interpolation followed by (2) feature extraction using Gray Level Co-Occurrence Matrix and Local Binary Pattern then (3) concatenation of all extracted features and finally (4) classification of Alzheimer disease stage using deep learning, Multi-Layer Perceptron, in particular. Our proposed method was tested using the MPRAGE structural MRI dataset from Alzheimer Disease Neuro Imaging Initiative (ADNI), and it outperformed other techniques used in the literature review. An accuracy result of 89.80 was reported for multi-class classification of 4 stages of Alzheimer disease (Cognitive Normal, Early Mild Cognitive Impairment, Late Mild Cognitive Impairment and Alzheimer Disease) for both Gray Matter (GM) and White Matter (WM). In term of binary-class classification, we were able to achieve very good results using both GM and WM. By using GM, we were able to distinguish between CN vs EMCI, EMCI vs AD and LMCI vs AD with accuracy results of 96.43%, 90.91% and 95.24% respectively. And using WM, we were able to distinguish between CN vs LMCI with 100% accuracy and EMCI vs LMCI with 95.65% accuracy. While we achieved the same accuracy result of 96.15 using both WM and GM.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3183185" target="_blank">https://dx.doi.org/10.1109/access.2022.3183185</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_e948c12a9269ff4a90ac3e80853ef377 |
| identifier_str_mv | 10.1109/access.2022.3183185 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24056394 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | An Exemplar Pyramid Feature Extraction Based Alzheimer Disease Classification MethodHeba Soliman Zaina (16904787)Samir Brahim Belhaouari (16855434)Tanya Stanko (16904790)Vladimir Gorovoy (16904793)EngineeringBiomedical engineeringInformation and computing sciencesData management and data scienceFeature extractionMagnetic resonance imagingAlzheimer's diseaseThree-dimensional displaysSupport vector machinesNonlinear distortionCognitive normal (CN)Early mild cognitive impairment (EMCI)Late mild cognitive impairment (LMCI)Alzheimer disease (AD)Local binary pattern (LBP)Gray level co-occurrence matrix (GLCM)Multi-layer perceptron (MLP)Exemplar pyramid<p>Dementia is a term used to describe a variety of symptoms related to cognitive impairment in which Alzheimer disease represents 60% – 70% of the cases. As of today, there is no cure for this disease and the only way to prevent any associated medical, economic, and financial impacts or losses is to detect the disease early and work closely with suspected patients to prevent any further progress. In this research, a methodology consisting of 4 modules is proposed: (1) preprocessing, exemplar pyramid along with bi-linear interpolation followed by (2) feature extraction using Gray Level Co-Occurrence Matrix and Local Binary Pattern then (3) concatenation of all extracted features and finally (4) classification of Alzheimer disease stage using deep learning, Multi-Layer Perceptron, in particular. Our proposed method was tested using the MPRAGE structural MRI dataset from Alzheimer Disease Neuro Imaging Initiative (ADNI), and it outperformed other techniques used in the literature review. An accuracy result of 89.80 was reported for multi-class classification of 4 stages of Alzheimer disease (Cognitive Normal, Early Mild Cognitive Impairment, Late Mild Cognitive Impairment and Alzheimer Disease) for both Gray Matter (GM) and White Matter (WM). In term of binary-class classification, we were able to achieve very good results using both GM and WM. By using GM, we were able to distinguish between CN vs EMCI, EMCI vs AD and LMCI vs AD with accuracy results of 96.43%, 90.91% and 95.24% respectively. And using WM, we were able to distinguish between CN vs LMCI with 100% accuracy and EMCI vs LMCI with 95.65% accuracy. While we achieved the same accuracy result of 96.15 using both WM and GM.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3183185" target="_blank">https://dx.doi.org/10.1109/access.2022.3183185</a></p>2022-06-15T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2022.3183185https://figshare.com/articles/journal_contribution/An_Exemplar_Pyramid_Feature_Extraction_Based_Alzheimer_Disease_Classification_Method/24056394CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240563942022-06-15T00:00:00Z |
| spellingShingle | An Exemplar Pyramid Feature Extraction Based Alzheimer Disease Classification Method Heba Soliman Zaina (16904787) Engineering Biomedical engineering Information and computing sciences Data management and data science Feature extraction Magnetic resonance imaging Alzheimer's disease Three-dimensional displays Support vector machines Nonlinear distortion Cognitive normal (CN) Early mild cognitive impairment (EMCI) Late mild cognitive impairment (LMCI) Alzheimer disease (AD) Local binary pattern (LBP) Gray level co-occurrence matrix (GLCM) Multi-layer perceptron (MLP) Exemplar pyramid |
| status_str | publishedVersion |
| title | An Exemplar Pyramid Feature Extraction Based Alzheimer Disease Classification Method |
| title_full | An Exemplar Pyramid Feature Extraction Based Alzheimer Disease Classification Method |
| title_fullStr | An Exemplar Pyramid Feature Extraction Based Alzheimer Disease Classification Method |
| title_full_unstemmed | An Exemplar Pyramid Feature Extraction Based Alzheimer Disease Classification Method |
| title_short | An Exemplar Pyramid Feature Extraction Based Alzheimer Disease Classification Method |
| title_sort | An Exemplar Pyramid Feature Extraction Based Alzheimer Disease Classification Method |
| topic | Engineering Biomedical engineering Information and computing sciences Data management and data science Feature extraction Magnetic resonance imaging Alzheimer's disease Three-dimensional displays Support vector machines Nonlinear distortion Cognitive normal (CN) Early mild cognitive impairment (EMCI) Late mild cognitive impairment (LMCI) Alzheimer disease (AD) Local binary pattern (LBP) Gray level co-occurrence matrix (GLCM) Multi-layer perceptron (MLP) Exemplar pyramid |