基于改进残差胶囊网络和麻雀搜索的脑瘤图像分类

Brain tumor image classification based on improved residual capsule network and sparrow search

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.