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- W4385434653 abstract "Alzheimer’s disease (AD) is a brain syndrome that gradually impairs our capacity to think rationally and do basic tasks. One of the earliest stages of AD is Mild Cognitive Impairment (MCI), where changes in brain tissues develop significantly. Although there has been much research going on, complex brain changes make the detection of AD very difficult. Deep Learning (DL) is a well-established Machine Learning (ML) technique used in medical image processing. Many DL models are successfully utilised in AD classification by various researchers. In this work, we have implemented some of the most commonly used DL models (VGG16, VGG19, Resnet, Inception and Xception) for Cognitively Normal (CN), AD and MCI classifications using brain MRIs. It can be seen from the experimental study that Xception outperforms other implemented models. To further increase the performance of Xception, we added one additional flattened layer that converts the multi-dimensional features into a single dimension. The proposed approach applies transfer learning to the multi-class classification of brain MRI images in classifying them. According to simulation findings, the suggested system model provides an accuracy of 83.34%, which is higher than that of the discussed models." @default.
- W4385434653 created "2023-08-01" @default.
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- W4385434653 date "2023-01-01" @default.
- W4385434653 modified "2023-09-25" @default.
- W4385434653 title "Implementation of Few Deep Learning Models to Detect Alzheimer’s Disease" @default.
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- W4385434653 doi "https://doi.org/10.1007/978-981-99-2680-0_38" @default.
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