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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2405.09553 |
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| _version_ | 1866909205195128832 |
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| author | Lazli, Lilia |
| author_facet | Lazli, Lilia |
| contents | Alzheimers disease (AD) is a severe neurological brain disorder. It is not curable, but earlier detection can help improve symptoms in a great deal. The machine learning based approaches are popular and well motivated models for medical image processing tasks such as computer-aided diagnosis. These techniques can improve the process for accurate diagnosis of AD. In this paper, we investigate the performance of these techniques for AD detection and classification using brain MRI and PET images from the OASIS database. The proposed system takes advantage of the artificial neural network and support vector machines as classifiers, and principal component analysis as a feature extraction technique. The results indicate that the combined scheme achieves good accuracy and offers a significant advantage over the other approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09553 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Computer aided diagnosis system for Alzheimers disease using principal component analysis and machine learning based approaches Lazli, Lilia Image and Video Processing Artificial Intelligence Alzheimers disease (AD) is a severe neurological brain disorder. It is not curable, but earlier detection can help improve symptoms in a great deal. The machine learning based approaches are popular and well motivated models for medical image processing tasks such as computer-aided diagnosis. These techniques can improve the process for accurate diagnosis of AD. In this paper, we investigate the performance of these techniques for AD detection and classification using brain MRI and PET images from the OASIS database. The proposed system takes advantage of the artificial neural network and support vector machines as classifiers, and principal component analysis as a feature extraction technique. The results indicate that the combined scheme achieves good accuracy and offers a significant advantage over the other approaches. |
| title | Computer aided diagnosis system for Alzheimers disease using principal component analysis and machine learning based approaches |
| topic | Image and Video Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2405.09553 |