一种基于多轴视觉变压器的新模型,用于在磁共振扫描中进行冠状腺骨诊断
Semih Demirel1, Okan Demirtaş2, Sümeyra Kuş Ordu2
1Department of Information Systems, Gazi University, Ankara, Turkey. semih.demirel1@gazi.edu.tr.
Physical and engineering sciences in medicine
|February 4, 2026
概括
这项研究引入了一种基于变压器的深度学习模型,用于从MRI扫描中诊断冠状腺 (CMP). 多轴视觉变压器在识别CMP方面取得了卓越的准确性,帮助早期诊断和治疗.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 整形外科 整形外科 整形外科
背景情况:
- 冠状马 (CMP) 是一种导致疼痛和残疾的退行性膝关节软骨疾病.
- 准确的诊断对于有效的治疗和疾病管理至关重要.
- 磁共振成像 (MRI) 是可视化膝盖结构的关键工具.
研究的目的:
- 开发和评估深度学习模型,以使用MRI数据进行自动化chondromalacia骨分类.
- 将基于变压器的模型与卷积神经网络模型的性能进行比较.
主要方法:
- 使用MRI数据集的患者和没有chondromalacia patella.
- 实施和评估了各种深度学习架构:多轴视觉转换器 (MaxViT),视觉转换器 (ViT),Swin转换器,googLeNet,ResNet18和MobileNetV2.2.
- 基于准确性,精度,回忆力和F1分数进行分类性能评估的模型.
主要成果:
- 多轴视觉变压器 (MaxViT) 在所有评估模型中表现出最高的性能.
- MaxViT的准确度为0.9817,精度为0.9821,回忆率为0.9817,F1得分为0.9818.1等.
- 与基于CNN的模型相比,基于变压器的模型通常显示出具有竞争力或优异的性能.
结论:
- 深度学习,特别是像MaxViT这样的变压器架构,显示出对MRI精确检测chondromalacia骨显著的希望.
- 自动分类可以支持及时诊断,潜在地改善患者膝关节疾病的结果.
- 进一步的研究可以探索将这些模型集成到临床工作流程中,以提高诊断能力.
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