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- W4295357811 abstract "The use of magnetic resonance imaging (MRI) in the diagnosis of dementia is significant because it reveals morphological differences induced by brain decline. The major obstacles faced by compuer assisted systems are: High dimensionality and unbalanced data. Numerous deep learning methods have been used on an MR image dataset for disease classification and prediction, but most of them have refused to endorse data dimensionality reduction, which resulted in skewed outcomes. The UCI machine learning repository provided the MR image of brain dataset utilised in this study. In this article, we suggest an productive dimensional reduction method to ameliorate the attainment of the dementia detection system.. The hybrid Linear Discriminant Analysis –Firefly algorithm (LDA-FA) is used for dimensionality reduction. On the reduced data set we apply an optimized convolutional neural network. The method we have proposed is less erroneous and more accurate than other recent work and also produced 95.21% accuracy." @default.
- W4295357811 created "2022-09-13" @default.
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- W4295357811 date "2022-01-01" @default.
- W4295357811 modified "2023-09-23" @default.
- W4295357811 title "Dimensionality Reduction Method for Early Detection of Dementia" @default.
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- W4295357811 doi "https://doi.org/10.1007/978-981-19-4831-2_2" @default.
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