Brain Stroke Classification Using Wavelet Transform and MLP Neural Networks on DWI MRI Images
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918062857388032 |
|---|---|
| author | Mohammadi, Mana Rad, Amirhesam Jafari Behrouzi, Ashkan |
| author_facet | Mohammadi, Mana Rad, Amirhesam Jafari Behrouzi, Ashkan |
| contents | This paper presents a lightweight framework for classifying brain stroke types from Diffusion-Weighted Imaging (DWI) MRI scans, employing a Multi-Layer Perceptron (MLP) neural network with Wavelet Transform for feature extraction. Accurate and timely stroke detection is critical for effective treatment and improved patient outcomes in neuroimaging. While Convolutional Neural Networks (CNNs) are widely used for medical image analysis, their computational complexity often hinders deployment in resource-constrained clinical settings. In contrast, our approach combines Wavelet Transform with a compact MLP to achieve efficient and accurate stroke classification. Using the "Brain Stroke MRI Images" dataset, our method yields classification accuracies of 82.0% with the "db4" wavelet (level 3 decomposition) and 86.00% with the "Haar" wavelet (level 2 decomposition). This analysis highlights a balance between diagnostic accuracy and computational efficiency, offering a practical solution for automated stroke diagnosis. Future research will focus on enhancing model robustness and integrating additional MRI modalities for comprehensive stroke assessment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15364 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Brain Stroke Classification Using Wavelet Transform and MLP Neural Networks on DWI MRI Images Mohammadi, Mana Rad, Amirhesam Jafari Behrouzi, Ashkan Image and Video Processing 92C55, 68T07 I.2.6; I.4.9; J.3 This paper presents a lightweight framework for classifying brain stroke types from Diffusion-Weighted Imaging (DWI) MRI scans, employing a Multi-Layer Perceptron (MLP) neural network with Wavelet Transform for feature extraction. Accurate and timely stroke detection is critical for effective treatment and improved patient outcomes in neuroimaging. While Convolutional Neural Networks (CNNs) are widely used for medical image analysis, their computational complexity often hinders deployment in resource-constrained clinical settings. In contrast, our approach combines Wavelet Transform with a compact MLP to achieve efficient and accurate stroke classification. Using the "Brain Stroke MRI Images" dataset, our method yields classification accuracies of 82.0% with the "db4" wavelet (level 3 decomposition) and 86.00% with the "Haar" wavelet (level 2 decomposition). This analysis highlights a balance between diagnostic accuracy and computational efficiency, offering a practical solution for automated stroke diagnosis. Future research will focus on enhancing model robustness and integrating additional MRI modalities for comprehensive stroke assessment. |
| title | Brain Stroke Classification Using Wavelet Transform and MLP Neural Networks on DWI MRI Images |
| topic | Image and Video Processing 92C55, 68T07 I.2.6; I.4.9; J.3 |
| url | https://arxiv.org/abs/2506.15364 |