摘要
本文提出了基于缩放重构残差胶囊网络和麻雀搜索的核磁共振成像(MRI)脑瘤图像分类方法。首先,针对图像质量差的MRI脑瘤图像,采用基于麻雀搜索的图像增强方法提升图片质量;其次,采用胶囊网络解决医疗图像数据量小、数据集不平衡的问题;最后,针对胶囊网络对于大尺寸图像产生的梯度消失和梯度爆炸问题,采用改进的残差网络提取尺寸较大图片的关键特征,使用缩放重构,降低模型体积,在避免过拟合的同时提高计算速度。实验结果验证了本文提出的模型在小样本、低质量、大尺寸MRI脑瘤图像分类问题上的有效性。
A magnetic resonance imaging(MRI)brain tumor image classification method based on sparrow search algorithm and scaled reconstruction residual capsule network was proposed. Firstly,for MRI brain tumor images with poor image quality,an image enhancement method based on sparrow search was taken to improve the image quality. Secondly,the capsule network was used to achieve better results on the small data volume and unbalanced medical dataset. Finally,in view of the gradient disappearance and gradient explosion problems of the capsule network for large size images,an improved residual network was used to extract the key features of large size images. Meanwhile,by using scaled reconstruction,the volume of model decreased while the calculation speed increased. The experimental results verify the effectiveness of the proposed method in the classification of small samples,low-quality,large size MRI brain tumor images.
作者
王生生
李晨旭
王翔宇
姚志林
刘一申
吴佳倩
杨晴然
Sheng-sheng WANG;Chen-xu LI;Xiang-yu WANG;Zhi-lin YAO;Yi-shen LIU;Jia-qian WU;Qing-ran YANG(College of Computer Science and Technology,Jilin University,Changchun 130012,China;High School Attached to Northeast Normal University,Changchun 130021,China)
出处
《吉林大学学报(工学版)》
EI
CAS
CSCD
北大核心
2022年第11期2653-2661,共9页
Journal of Jilin University:Engineering and Technology Edition
基金
国家重点研发计划项目(2020YFA0714103)
国家自然科学基金区域创新发展联合基金项目(U19A2061)
吉林省发展和改革委员会创新能力建设项目(2021FGWCXNLJSSZ10,2019C053-3)。
关键词
计算机应用技术
脑瘤图像分类
麻雀搜索算法
残差网络
胶囊网络
动态路由算法
computer application technology
brain tumor classification
sparrow search algorithm
residual network
capsule network
dynamic routing agreement