协助-皮肤病:一个轻量级可分离的视觉转换器模型,用于多类皮肤损伤分类
Qaisar Abbas1, Yassine Daadaa1, Umer Rashid2
1College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|August 12, 2023
概括
本研究介绍了Assist-Dermo,这是一个高效的深度学习系统,用于分类有色素皮肤病变 (PSL). 它在早期检测皮肤癌方面取得了很高的准确性,以更少的参数优于现有的方法.
科学领域:
- 皮肤病学和人工智能研究
- 医学图像分析 医学图像分析
- 计算机视觉用于医疗保健
背景情况:
- 彩色皮肤病变 (PSL) 的自动分类有助于早期发现皮肤癌.
- 现有的系统往往需要大量的计算资源,限制在受限制的设备上部署.
- 对于PSL分类,需要准确,但计算效率高的深度学习模型.
研究的目的:
- 开发一个自动分类系统,Assist-Dermo,用于识别PSL的九个类.
- 设计一个可分离视觉变压器 (SVT) 架构,参数较少,准确度与最先进的 (SOTA) 模型相比.
- 为了提高临床专家的运行时间性能和诊断效率.
主要方法:
- 开发了一个新的SVT架构,集成SqueezeNet和深度可分离的卷积神经网络 (CNN) 模型.
- 使用数据增强来解决PSL不平衡和预处理来选择和增强损伤区域.
- 利用了包括Ph2,ISBI-2017,HAM10000和ISIC在内的多种数据集进行培训和评估.
主要成果:
- 实现了高性能指标:95.6%的精度 (ACC),96.7%的灵敏度 (SE),95%的特异性 (SP) 和0.95的曲线下面面积 (AUC).
- 与SOTA算法相比,Assist-Dermo系统在分类9个PSL类中表现出优越的性能.
- 模型的效率通过其减少的参数数量和改进的运行时间性能来验证.
结论:
- 该Assist-Dermo系统提供了一个计算效率高,准确的解决方案,用于分类有色素的皮肤病变.
- 它有效地协助皮肤科医生通过皮肤镜检测皮肤癌的早期发现.
- 开发的模型代码在GitHub上公开提供,促进进一步的研究和开发.
关键词:
这就是SqueezeNet.这是分类分类的分类.深度学习是一种深度学习.在深度上可分离的CNN CNN.皮肤显微镜 (dermoscopy) 是一种可以检查皮肤的方法.有色素的皮肤病变.皮肤癌是皮肤癌.视觉变压器 视觉变压器更多相关视频
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