# Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis

arXiv: https://arxiv.org/abs/2403.16212 · HTML: https://mlsanity.com/paper/2403.16212/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 24 Mar 2024.

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

- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation](https://mlsanity.com/paper/2401.06167/) (2024)
- [Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection](https://mlsanity.com/paper/2403.02232/) (2024)

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

- [Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) data](https://mlsanity.com/paper/2407.00028/) (2024)
- [Breast Cancer Image Classification Method Based on Deep Transfer Learning](https://mlsanity.com/paper/2404.09226/) (2024)
- [Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising](https://mlsanity.com/paper/2406.10239/) (2024)
- [A Deep Multiscale Neural Network for Accurate Neurological Disorder Detection from MRI Scans and Real-Time Web Deployment](https://mlsanity.com/paper/2606.29106/) (2026)

## Abstract (arXiv)

Exploring the application of deep learning technologies in the field of medical diagnostics, Magnetic Resonance Imaging (MRI) provides a unique perspective for observing and diagnosing complex neurodegenerative diseases such as Alzheimer Disease (AD). With advancements in deep learning, particularly in Convolutional Neural Networks (CNNs) and the Xception network architecture, we are now able to analyze and classify vast amounts of MRI data with unprecedented accuracy. The progress of this technology not only enhances our understanding of brain structural changes but also opens up new avenues for monitoring disease progression through non-invasive means and potentially allows for precise diagnosis in the early stages of the disease. This study aims to classify MRI images using deep learning models to identify different stages of Alzheimer Disease through a series of innovative data processing and model construction steps. Our experimental results show that the deep learning framework based on the Xception model achieved a 99.6% accuracy rate in the multi-class MRI image classification task, demonstrating its potential application value in assistive diagnosis. Future research will focus on expanding the dataset, improving model interpretability, and clinical validation to further promote the application of deep learning technology in the medical field, with the hope of bringing earlier diagnosis and more personalized treatment plans to Alzheimer Disease patients.