摘要
针对不同模态间对应特征极易融合错位、识别模型专家主观经验式调参且计算成本高等问题,提出自优化双模态(“对比增强T1加权”与“高分辨率增强T2加权”)多通路非深度前庭神经鞘瘤识别模型。首先,通过构建前庭神经鞘瘤识别模型进一步挖掘前庭神经鞘瘤病症多模态影像特征及模态间复杂的非线性互补信息;其次,设计基于博弈论全局并行麻雀搜索算法的模型优化策略,实现模型关键超参数的自适应寻优,使模型具有较优的识别效果。实验结果表明,相较于基于深度学习的模型,所提模型在识别准确率提升4.19个百分点的情况下参数量降低了27.9%,验证了它的有效性和自适应性。
Aiming at the problems of the corresponding features between different modals easy to be fused and mislocated,the subjective empirical parameter adjustment of recognition model experts,and the high computational cost,a self-optimized dual-modal(“contrast enhanced T1 weighting”and“high resolution enhanced T2 weighting”)multi-channel non-deep vestibular schwannoma recognition model was proposed.Firstly,a vestibular schwannoma recognition model was constructed to further explore the multi-modal image features of vestibular schwannoma and the complex nonlinear complementary information among the modals.Then,a model optimization strategy with global parallel sparrow search algorithm based on game theory was designed to realize the adaptive optimization of key hyperparameters of the model,so that the model had a better recognition effect.Experimental results show that compared with the deep learning-based model,the proposed model reduces the number of parameters by 27.9%with an improvement of 4.19 percentage points in recognition accuracy,which verifies the effectiveness and adaptability of the proposed model.
作者
张睿
张鹏云
高美蓉
ZHANG Rui;ZHANG Pengyun;GAO Meirong(College of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan Shanxi 030024,China)
出处
《计算机应用》
CSCD
北大核心
2024年第9期2975-2982,共8页
journal of Computer Applications
基金
山西省基础研究计划项目(20210302123216)
太原科技大学研究生联合培养示范基地项目(JD2022004)
太原科技大学研究生教育创新项目(SY2023040)。
关键词
前庭神经鞘瘤
多模态神经网络
非深度模型
并行加速
模型自优化
vestibular schwannoma
multi-modal neural network
non-deep model
parallel acceleration
model selfoptimization
