Deep learning based prediction of Alzheimer's disease from magnetic resonance images

2021/01/13 by Manu Subramoniam, Subramoniam, Manu, T. R. Aparna +7
Computer Science · Engineering · Health Professions · Neuroscience · #AI in cancer detection #Artificial Intelligence in Healthcare #Brain Tumor Detection and Classification #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Signal Processing (eess.SP) #eess.IV #eess.SP #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2101.04961

Some errors in the results section, which has to be corrected

openalex publication_date 2021/01/13 · arxiv created 2021/05/14 · arxiv updated 2021/05/17 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28

Abstract

Alzheimer's disease (AD) is an irreversible, progressive neuro degenerative disorder that slowly destroys memory and thinking skills and eventually, the ability to carry out the simplest tasks. In this paper, a deep neural network based prediction of AD from magnetic resonance images (MRI) is proposed. The state of the art image classification networks like VGG, residual networks (ResNet) etc. with transfer learning shows promising results. Performance of pre-trained versions of these networks are improved by transfer learning. ResNet based architecture with large number of layers is found to give the best result in terms of predicting different stages of the disease. The experiments are conducted on Kaggle dataset.

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