摘要
脑肿瘤是世界上最致命的癌症之一.由于脑肿瘤的多样性,其图像分类成为了当代研究的热点.近年来,深度神经网络(DNN)常用于医学图像分类,但随着深度的增加网络会出现梯度消失和过拟合的问题,而残差网络(ResNet)通过引入恒等映射可以缓解这些问题.因此,本文基于ResNet提出了一种MDM-ResNet网络,该网络由多尺寸卷积核模块(Multi-size convolution kernel module)、双通道池化层(Dual-channel pooling layer)和多深度融合残差块(Multi-depth fusion residual block)组成.本文实验在Figshare数据集上展开,采用数据增强操作对图像进行预处理,并利用5倍交叉验证方法对网络性能进行评估.最终实验结果表明MDM-ResNet能够对脑膜瘤(Meningioma)、胶质瘤(Glioma)和垂体瘤(Pituitary tumor)进行有效分类.
Brain tumor is one of the most fatal cancers in the world.Its image classification has become the hot spot due to the diverse characteristics of brain tumors.In recent years, Deep Neural Networks(DNN) are commonly used for medical image classification, but the problem of gradient vanishing and over fitting will appear with the increase of depth, while the Residual Network(ResNet) can solve this problem by introducing identity mapping.Therefore, this paper proposes an MDM-ResNet approach for brain tumor classification, which is composed of multi-size convolution kernel module, dual-channel pooling layer and multi-depth fusion residual block.The experiment was carried out on Figshare dataset, the image was preprocessed by data enhancement operation, and the performance of network was evaluated based on five-fold cross validation.The experimental results prove that the MDM-ResNet approach can effectively classify meningioma, glioma and pituitary tumor.
作者
夏景明
邢露萍
谈玲
宣大伟
XIA Jingming;XING Luping;TAN Ling;XUAN Dawei(School of Artificial Intelligence,Nanjing University of Information Science&Technology,Nanjing 210044;School of Computer and Software,Nanjing University of Information Science&Technology,Nanjing 210044)
出处
《南京信息工程大学学报（自然科学版）》
CAS
北大核心
2022年第2期212-219,共8页
Journal of Nanjing University of Information Science & Technology（Natural Science Edition）
基金
国家自然科学基金(41505017)。
