# Binary Classification of Alzheimer Disease using sMRI Imaging modality and Deep Learning

arXiv: https://arxiv.org/abs/1809.06209 · HTML: https://mlsanity.com/paper/1809.06209/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2018.

## Built on by (most cited first, 9 of 9)

- [Deep Learning-based Eye-Tracking Analysis for Diagnosis of Alzheimer's Disease Using 3D Comprehensive Visual Stimuli](https://mlsanity.com/paper/2303.06868/) (2023)
- [Interpretable Alzheimer's Disease Classification Via a Contrastive Diffusion Autoencoder](https://mlsanity.com/paper/2306.03022/) (2023)
- [A review of handcrafted and deep radiomics in neurological diseases: transitioning from oncology to clinical neuroimaging](https://mlsanity.com/paper/2407.13813/) (2024)
- [Gradient flows and randomised thresholding: sparse inversion and classification](https://mlsanity.com/paper/2203.11555/) (2022)
- [Deep Learning in Early Alzheimer's disease's Detection: A Comprehensive Survey of Classification, Segmentation, and Feature Extraction Methods](https://mlsanity.com/paper/2501.15293/) (2025)
- [Multimodal Identification of Alzheimer's Disease: A Review](https://mlsanity.com/paper/2311.12842/) (2023)
- [Attention-based Efficient Classification for 3D MRI Image of Alzheimer's Disease](https://mlsanity.com/paper/2401.14130/) (2024)
- [Introducing DEFORMISE: A deep learning framework for dementia diagnosis in the elderly using optimized MRI slice selection](https://mlsanity.com/paper/2407.17324/) (2024)
- [3D MRI-Based Alzheimer's Disease Classification Using Multi-Modal 3D CNN with Leakage-Aware Subject-Level Evaluation](https://mlsanity.com/paper/2603.17304/) (2026)

## Abstract (arXiv)

Alzheimer's disease (AD) is an irreversible devastative neurodegenerative disorder associated with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for the development of possible future treatment option(s). Structural magnetic resonance images (sMRI) plays an important role to help in understanding the anatomical changes related to AD especially in its early stages. Conventional methods require the expertise of domain experts and extract hand-picked features such as gray matter substructures and train a classifier to distinguish AD subjects from healthy subjects. Different from these methods, this paper proposes to construct multiple deep 2D convolutional neural networks (2D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis. The whole brain image was passed through two transfer learning architectures; Inception version 3 and Xception; as well as custom Convolutional Neural Network (CNN) built with the help of separable convolutional layers which can automatically learn the generic features from imaging data for classification. Our study is conducted using cross-sectional T1-weighted structural MRI brain images from Open Access Series of Imaging Studies (OASIS) database to maintain the size and contrast over different MRI scans. Experimental results show that the transfer learning approaches exceed the performance of non-transfer learning based approaches demonstrating the effectiveness of these approaches for the binary AD classification task.