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- W4310585464 abstract "The term dementia is used to describe a number of different conditions affecting the brain. Dementia interferes with a person's daily life and activities as the patient has difficulty remembering events or conversations. A deep learning (DL) framework can be used to detect dementia using MRI scans. But a paucity of MRI scans that are needed to train the deep learning model is the main challenge in DL-based dementia detection. To address this issue, we use the transfer learning technique with fine tuning to detect and classify the severity of dementia using MRI scan images. The dataset used in this paper is obtained from open-source platform which consists of different severity levels of MRI images. The data imbalance is managed with data augmentation techniques to avoid the overfitting of the model. The model performance is assessed on the accuracy, precision, recall, F-1 score, and confusion matrix. The proposed technique achieved an overall accuracy of 97.66%." @default.
- W4310585464 created "2022-12-12" @default.
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- W4310585464 date "2022-10-26" @default.
- W4310585464 modified "2023-09-28" @default.
- W4310585464 title "Multi-Class Classification of Dementia from MRI Images Using Transfer Learning" @default.
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- W4310585464 doi "https://doi.org/10.1109/uemcon54665.2022.9965672" @default.
- W4310585464 hasPublicationYear "2022" @default.
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