Early prediction of Alzheimer's disease using convolutional neural network: a review
Abstract In this paper, a comprehensive review on Alzheimer's disease (AD) is carried out, and an exploration of the two machine learning (ML) methods that help to identify the disease in its initial stages. Alzheimer's disease is a neurocognitive disorder occurring in people in their earl...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-11-01
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| Series: | The Egyptian Journal of Neurology, Psychiatry and Neurosurgery |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41983-022-00571-w |
| _version_ | 1811217719623680000 |
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| author | Vijeeta Patil Manohar Madgi Ajmeera Kiran |
| author_facet | Vijeeta Patil Manohar Madgi Ajmeera Kiran |
| author_sort | Vijeeta Patil |
| collection | DOAJ |
| description | Abstract In this paper, a comprehensive review on Alzheimer's disease (AD) is carried out, and an exploration of the two machine learning (ML) methods that help to identify the disease in its initial stages. Alzheimer's disease is a neurocognitive disorder occurring in people in their early onset. This disease causes the person to suffer from memory loss, unusual behavior, and language problems. Early detection is essential for developing more advanced treatments for AD. Machine learning (ML), a subfield of Artificial Intelligence (AI), uses various probabilistic and optimization techniques to help computers learn from huge and complicated data sets. To diagnose AD in its early stages, researchers generally use machine learning. The survey provides a broad overview of current research in this field and analyses the classification methods used by researchers working with ADNI data sets. It discusses essential research topics such as the data sets used, the evaluation measures employed, and the machine learning methods used. Our presentation suggests a model that helps better understand current work and highlights the challenges and opportunities for innovative and useful research. The study shows which machine learning method holds best for the ADNI data set. Therefore, the focus is given to two methods: the 18-layer convolutional network and the 3D convolutional network. Hence, CNNs with multi-layered fetch more accurate results as compared to 3D CNN. The work also contributes to the use of the ADNI data set, where the classification of training and testing samples is divided with such a number that brings the highest accuracy achieved with 18-layer CNN. The work concentrates on the early prediction of Alzheimer's disease with machine learning methods. Thus, the accuracy achieved is 98% for 18-layer CNN. |
| first_indexed | 2024-04-12T06:59:29Z |
| format | Article |
| id | doaj.art-ad9f6db76c414ec2838e472a4cc5aa56 |
| institution | Directory Open Access Journal |
| issn | 1687-8329 |
| language | English |
| last_indexed | 2024-04-12T06:59:29Z |
| publishDate | 2022-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | The Egyptian Journal of Neurology, Psychiatry and Neurosurgery |
| spelling | doaj.art-ad9f6db76c414ec2838e472a4cc5aa562022-12-22T03:43:03ZengSpringerOpenThe Egyptian Journal of Neurology, Psychiatry and Neurosurgery1687-83292022-11-0158111010.1186/s41983-022-00571-wEarly prediction of Alzheimer's disease using convolutional neural network: a reviewVijeeta Patil0Manohar Madgi1Ajmeera Kiran2Department of Computer Science and Engineering, KLE Institute of TechnologyDepartment of Computer Science and Engineering, KLE Institute of TechnologyDepartment of Computer Science and Engineering, MLR Institute of TechnologyAbstract In this paper, a comprehensive review on Alzheimer's disease (AD) is carried out, and an exploration of the two machine learning (ML) methods that help to identify the disease in its initial stages. Alzheimer's disease is a neurocognitive disorder occurring in people in their early onset. This disease causes the person to suffer from memory loss, unusual behavior, and language problems. Early detection is essential for developing more advanced treatments for AD. Machine learning (ML), a subfield of Artificial Intelligence (AI), uses various probabilistic and optimization techniques to help computers learn from huge and complicated data sets. To diagnose AD in its early stages, researchers generally use machine learning. The survey provides a broad overview of current research in this field and analyses the classification methods used by researchers working with ADNI data sets. It discusses essential research topics such as the data sets used, the evaluation measures employed, and the machine learning methods used. Our presentation suggests a model that helps better understand current work and highlights the challenges and opportunities for innovative and useful research. The study shows which machine learning method holds best for the ADNI data set. Therefore, the focus is given to two methods: the 18-layer convolutional network and the 3D convolutional network. Hence, CNNs with multi-layered fetch more accurate results as compared to 3D CNN. The work also contributes to the use of the ADNI data set, where the classification of training and testing samples is divided with such a number that brings the highest accuracy achieved with 18-layer CNN. The work concentrates on the early prediction of Alzheimer's disease with machine learning methods. Thus, the accuracy achieved is 98% for 18-layer CNN.https://doi.org/10.1186/s41983-022-00571-wAlzheimer's diseaseEarly detectionConvolution neural networkMRI imagesMachine learningDenseNet169 |
| spellingShingle | Vijeeta Patil Manohar Madgi Ajmeera Kiran Early prediction of Alzheimer's disease using convolutional neural network: a review The Egyptian Journal of Neurology, Psychiatry and Neurosurgery Alzheimer's disease Early detection Convolution neural network MRI images Machine learning DenseNet169 |
| title | Early prediction of Alzheimer's disease using convolutional neural network: a review |
| title_full | Early prediction of Alzheimer's disease using convolutional neural network: a review |
| title_fullStr | Early prediction of Alzheimer's disease using convolutional neural network: a review |
| title_full_unstemmed | Early prediction of Alzheimer's disease using convolutional neural network: a review |
| title_short | Early prediction of Alzheimer's disease using convolutional neural network: a review |
| title_sort | early prediction of alzheimer s disease using convolutional neural network a review |
| topic | Alzheimer's disease Early detection Convolution neural network MRI images Machine learning DenseNet169 |
| url | https://doi.org/10.1186/s41983-022-00571-w |
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