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

Source: [Deep learning based prediction of Alzheimer's disease from magnetic resonance images](<https://www.sota2.com/research/paper/68fa7f8f1e209131cea9a7f5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T06:50:43.062Z

Authors: Manu Subramoniam, Aparna T. R., Anurenjan P. R., Sreeni K. G

Published: 2021-01-13T09:46:25.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2101.04961>)

[PDF](<https://arxiv.org/pdf/2101.04961.pdf>)