用小模型+小波变换，快速准确区分脑卒中类型
Brain Stroke Classification Using Wavelet Transform and MLP Neural Networks on DWI MRI Images
- 先用小波变换提取DWI MRI图像特征，再用轻量MLP分类
- 哈爾小波在2层分解下准确率达86.00%，达芬特小波3层为82.0%
- 适合资源有限的临床环境，部署快、耗能低
本文提出一种轻量级框架，用于从扩散加权成像（DWI）MRI扫描中分类脑卒中类型。采用多层感知机（MLP）神经网络结合小波变换进行特征提取。准确及时的卒中检测对治疗和患者预后至关重要。尽管卷积神经网络（CNNs）广泛用于医学图像分析，但其计算复杂性常限制其在资源受限的临床场景中的应用。本方法将小波变换与紧凑型MLP结合，实现了高效且准确的卒中分类。使用“Brain Stroke MRI Images”数据集，采用“db4”小波（3层分解）时分类准确率为82.0%，采用“Haar”小波（2层分解）时准确率为86.00%。该分析展示了诊断精度与计算效率之间的平衡，为自动化卒中诊断提供了实用方案。未来研究将聚焦于提升模型鲁棒性，并整合其他磁共振成像模态以实现全面卒中评估。
原文摘要 · Abstract (English)
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.
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