# Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data

arXiv: https://arxiv.org/abs/2101.02876 · HTML: https://mlsanity.com/paper/2101.02876/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Medical Image Analysis using Convolutional Neural Networks: A Review](https://mlsanity.com/paper/1709.02250/) (2017)
- [Towards Alzheimer's Disease Classification through Transfer Learning](https://mlsanity.com/paper/1711.11117/) (2017)
- [An Ensemble of Deep Convolutional Neural Networks for Alzheimer's Disease Detection and Classification](https://mlsanity.com/paper/1712.01675/) (2017)

## Built on by (most cited first, 4 of 4)

- [TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs](https://mlsanity.com/paper/2411.03464/) (2024)
- [AlzhiNet: Traversing from 2DCNN to 3DCNN, Towards Early Detection and Diagnosis of Alzheimer's Disease](https://mlsanity.com/paper/2410.02714/) (2024)
- [Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement](https://mlsanity.com/paper/2607.22770/) (2026)
- [Technological Advances in Detecting and Managing Cognitive Impairment in Older Adults: Trends, Challenges, and Future Directions](https://mlsanity.com/paper/2607.28687/) (2026)

## Abstract (arXiv)

Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient's memory. An early detection can prevent the patient from further damage of the brain cells and hence avoid permanent memory loss. In past few years, various automatic tools and techniques have been proposed for diagnosis of AD. Several methods focus on fast, accurate and early detection of the disease to minimize the loss to patients mental health. Although machine learning and deep learning techniques have significantly improved medical imaging systems for AD by providing diagnostic performance close to human level. But the main problem faced during multi-class classification is the presence of highly correlated features in the brain structure. In this paper, we have proposed a smart and accurate way of diagnosing AD based on a two-dimensional deep convolutional neural network (2D-DCNN) using imbalanced three-dimensional MRI dataset. Experimental results on Alzheimer Disease Neuroimaging Initiative magnetic resonance imaging (MRI) dataset confirms that the proposed 2D-DCNN model is superior in terms of accuracy, efficiency, and robustness. The model classifies MRI into three categories: AD, mild cognitive impairment, and normal control: and has achieved 99.89% classification accuracy with imbalanced classes. The proposed model exhibits noticeable improvement in accuracy as compared to the state-fo-the-art methods.