Abstract
Alzheimer’s disease (AD) destroys memory and the ability to think in a rational way due to neurodegeneration. This degeneration is progressive as well as irreversible. It ultimately affects a person’s ability to do even trivial tasks. In this paper, we propose a neural predictor of AD from brain imaging done using magnetic resonance images (MRI) technology. Image classification networks like VGG, residual networks (ResNet), etc. with transfer learning show good results. These network architectures are taken and re-trained using brain images. It is shown that a deep ResNet neural architecture performs better in terms of accuracy. Kaggle dataset was used as the dataset to conduct our experiments.
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Subramoniam, M., Aparna, T.R., Anurenjan, P.R., Sreeni, K.G. (2022). Deep Learning-Based Prediction of Alzheimer’s Disease from Magnetic Resonance Images. In: Saraswat, M., Sharma, H., Arya, K.V. (eds) Intelligent Vision in Healthcare. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-16-7771-7_12
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DOI: https://doi.org/10.1007/978-981-16-7771-7_12
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