用视觉变压器+双向GRU模型，94%准确率自动识别脑卒中。
Stro-VIGRU: Defining the Vision Recurrent-Based Baseline Model for Brain Stroke Classification
- 冻结ViT部分编码器，微调其余层提取脑卒中特征。
- 在脑卒中数据集上达到94.06%分类准确率。
- 适合医学影像分析、早期疾病筛查的研究者参考。
脑卒中是全球导致死亡和残疾的主要原因，早期识别对成功治疗至关重要。目前常用CT扫描诊断脑卒中，虽快速易得，但人工分析耗时且易出错。本文提出一种基于预训练视觉变换器的迁移学习框架，用于早期脑卒中识别。冻结ViT模型部分编码器块，其余部分进行微调以学习脑卒中特异性特征。提取的特征输入单层双向GRU完成分类。通过数据增强处理类别不平衡问题。该模型在脑卒中数据集上实现94.06%的分类准确率。
原文摘要 · Abstract (English)
Stroke majorly causes death and disability worldwide, and early recognition is one of the key elements of successful treatment of the same. It is common to diagnose strokes using CT scanning, which is fast and readily available, however, manual analysis may take time and may result in mistakes. In this work, a pre-trained Vision Transformer-based transfer learning framework is proposed for the early identification of brain stroke. A few of the encoder blocks of the ViT model are frozen, and the rest are allowed to be fine-tuned in order to learn brain stroke-specific features. The features that have been extracted are given as input to a single-layer Bi-GRU to perform classification. Class imbalance is handled by data augmentation. The model has achieved 94.06% accuracy in classifying brain stroke from the Stroke Dataset.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。