Early diagnosis of Alzheimer's disease from MRI images with deep learning model
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909327861743616 |
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| author | Javid, Sajjad Aghasi Feghhi, Mahmood Mohassel |
| author_facet | Javid, Sajjad Aghasi Feghhi, Mahmood Mohassel |
| contents | It is acknowledged that the most common cause of dementia worldwide is Alzheimer's disease (AD). This condition progresses in severity from mild to severe and interferes with people's everyday routines. Early diagnosis plays a critical role in patient care and clinical trials. Convolutional neural networks (CNN) are used to create a framework for identifying specific disease features from MRI scans Classification of dementia involves approaches such as medical history review, neuropsychological tests, and magnetic resonance imaging (MRI). However, the image dataset obtained from Kaggle faces a significant issue of class imbalance, which requires equal distribution of samples from each class to address. In this article, to address this imbalance, the Synthetic Minority Oversampling Technique (SMOTE) is utilized. Furthermore, a pre-trained convolutional neural network has been applied to the DEMNET dementia network to extract key features from AD images. The proposed model achieved an impressive accuracy of 98.67%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18814 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Early diagnosis of Alzheimer's disease from MRI images with deep learning model Javid, Sajjad Aghasi Feghhi, Mahmood Mohassel Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning It is acknowledged that the most common cause of dementia worldwide is Alzheimer's disease (AD). This condition progresses in severity from mild to severe and interferes with people's everyday routines. Early diagnosis plays a critical role in patient care and clinical trials. Convolutional neural networks (CNN) are used to create a framework for identifying specific disease features from MRI scans Classification of dementia involves approaches such as medical history review, neuropsychological tests, and magnetic resonance imaging (MRI). However, the image dataset obtained from Kaggle faces a significant issue of class imbalance, which requires equal distribution of samples from each class to address. In this article, to address this imbalance, the Synthetic Minority Oversampling Technique (SMOTE) is utilized. Furthermore, a pre-trained convolutional neural network has been applied to the DEMNET dementia network to extract key features from AD images. The proposed model achieved an impressive accuracy of 98.67%. |
| title | Early diagnosis of Alzheimer's disease from MRI images with deep learning model |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2409.18814 |