基于卷积神经网络的脑胶质瘤MR图像诊断与分类

Fatemeh Bashir Gonbadi, Hassan Khotanlou
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引用次数: 5

摘要

脑肿瘤分析是医学图像处理的一个重要领域。胶质瘤是一种起源于神经胶质细胞的威胁性脑肿瘤,根据世界卫生组织(WHO)将其分为两个级别。本文提出了一种基于卷积神经网络(CNN)的新方法,将磁共振成像(MRI)图像中的胶质瘤肿瘤分为正常脑、高级别胶质瘤和低级别胶质瘤三类。该方法包括预处理单元和网络两部分。预处理单元从颅骨中提取大脑图像,并将得到的图像送入CNN网络进行分类。该网络从图像中提取主要特征并创建特征图。然后,网络的第二部分从特征图中提取次要特征,并对其进行分类。本文使用的数据集是IXI数据集作为正常脑图像,BRATS2017数据集作为胶质瘤图像。该方法将MRI图像分为三类,准确率达到99.18%。
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Glioma Brain Tumors Diagnosis and Classification in MR Images based on Convolutional Neural Networks
Brain tumor analysis is a critical field in medical image processing. Glioma is one of the threatening brain tumors originating from glial cells and is divided into two grades according to the World Health Organization (WHO). In this paper, a novel method based on Convolutional Neural Networks (CNN) is presented to diagnose and classify Glioma tumors in Magnetic Resonance Imaging (MRI) images into three classes: Normal Brain, High-Grade Glioma and Low-Grade Glioma. The proposed method includes 2 parts: preprocessing unit and network. Preprocessing unit extracts brain from skull and the obtained image is fed into a CNN network to be classified. The network extracts primary features from images and creates feature maps. Then the second part of the network extracts secondary features from the feature maps and finally classifies them. The datasets used in this paper are IXI dataset as normal brain images and BRATS2017 dataset as Glioma tumor images. This method classifies the MRI images into three categories, performed with a desirable accuracy of 99.18%.
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