A proficient approach for the classification of Alzheimer’s disease using a hybridization of machine learning and deep learning
Abstract Alzheimer’s disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the nervous system, affecting the cognitive ability of the human brain. Over the past two decades, neuroimaging data from Magnetic Resonance Imaging (MRI) scans has been increasingly used in the...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-81563-z |
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| author | Hafiz Ahmed Raza Shahab U. Ansari Kamran Javed Muhammad Hanif Saeed Mian Qaisar Usman Haider Paweł Pławiak Iffat Maab |
| author_facet | Hafiz Ahmed Raza Shahab U. Ansari Kamran Javed Muhammad Hanif Saeed Mian Qaisar Usman Haider Paweł Pławiak Iffat Maab |
| author_sort | Hafiz Ahmed Raza |
| collection | DOAJ |
| description | Abstract Alzheimer’s disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the nervous system, affecting the cognitive ability of the human brain. Over the past two decades, neuroimaging data from Magnetic Resonance Imaging (MRI) scans has been increasingly used in the study of brain pathology related to the birth and growth of AD. Recent studies have employed machine learning to detect and classify AD. Deep learning models have also been increasingly utilized with varying degrees of success. This paper presents a novel hybrid approach for early detection and classification of AD using structural MRI (sMRI). The proposed model employs a unique combination of machine learning and deep learning approaches to optimize the precision and accuracy of the detection and classification of AD. The proposed approach surpassed multi-modal machine learning algorithms in accuracy, precision, and F-measure performance measures. Results confirm an outperformance compared to the state-of-the-art in AD versus CN and sMCI versus pMCI paradigms. Within the CN versus AD paradigm, the designed model achieves 91.84% accuracy on test data. |
| format | Article |
| id | doaj-art-9b9e4fba0de945b69b0da62ca8367ac8 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9b9e4fba0de945b69b0da62ca8367ac82025-08-20T02:43:36ZengNature PortfolioScientific Reports2045-23222024-12-0114111410.1038/s41598-024-81563-zA proficient approach for the classification of Alzheimer’s disease using a hybridization of machine learning and deep learningHafiz Ahmed Raza0Shahab U. Ansari1Kamran Javed2Muhammad Hanif3Saeed Mian Qaisar4Usman Haider5Paweł Pławiak6Iffat Maab7Artificial Intelligence in Medicine (AIM) Lab, GIK Institute of Engineering Sciences and TechnologyArtificial Intelligence in Medicine (AIM) Lab, GIK Institute of Engineering Sciences and TechnologyNational Centre of Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA)Artificial Intelligence in Medicine (AIM) Lab, GIK Institute of Engineering Sciences and TechnologyCollege of Engineering and Technology, American University of the Middle EastDepartment of AI and DS, National University of Computer and Emerging Sciences, FAST-NUCESDepartment of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of TechnologyDepartment of Technology Management for Innovation, University of TokyoAbstract Alzheimer’s disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the nervous system, affecting the cognitive ability of the human brain. Over the past two decades, neuroimaging data from Magnetic Resonance Imaging (MRI) scans has been increasingly used in the study of brain pathology related to the birth and growth of AD. Recent studies have employed machine learning to detect and classify AD. Deep learning models have also been increasingly utilized with varying degrees of success. This paper presents a novel hybrid approach for early detection and classification of AD using structural MRI (sMRI). The proposed model employs a unique combination of machine learning and deep learning approaches to optimize the precision and accuracy of the detection and classification of AD. The proposed approach surpassed multi-modal machine learning algorithms in accuracy, precision, and F-measure performance measures. Results confirm an outperformance compared to the state-of-the-art in AD versus CN and sMCI versus pMCI paradigms. Within the CN versus AD paradigm, the designed model achieves 91.84% accuracy on test data.https://doi.org/10.1038/s41598-024-81563-zAlzheimer’s diseaseClassificationMachine learningConvolutional neural networkHybrid features learning |
| spellingShingle | Hafiz Ahmed Raza Shahab U. Ansari Kamran Javed Muhammad Hanif Saeed Mian Qaisar Usman Haider Paweł Pławiak Iffat Maab A proficient approach for the classification of Alzheimer’s disease using a hybridization of machine learning and deep learning Scientific Reports Alzheimer’s disease Classification Machine learning Convolutional neural network Hybrid features learning |
| title | A proficient approach for the classification of Alzheimer’s disease using a hybridization of machine learning and deep learning |
| title_full | A proficient approach for the classification of Alzheimer’s disease using a hybridization of machine learning and deep learning |
| title_fullStr | A proficient approach for the classification of Alzheimer’s disease using a hybridization of machine learning and deep learning |
| title_full_unstemmed | A proficient approach for the classification of Alzheimer’s disease using a hybridization of machine learning and deep learning |
| title_short | A proficient approach for the classification of Alzheimer’s disease using a hybridization of machine learning and deep learning |
| title_sort | proficient approach for the classification of alzheimer s disease using a hybridization of machine learning and deep learning |
| topic | Alzheimer’s disease Classification Machine learning Convolutional neural network Hybrid features learning |
| url | https://doi.org/10.1038/s41598-024-81563-z |
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