Convolutional-Neural-Network Assisted Segmentation and SVM Classification of Brain Tumor in Clinical MRI Slices
Convolutional-Neural-Network Assisted Segmentation and SVM Classification of Brain Tumor in Clinical MRI Slices
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DOI：
10.5755/j01.itc.50.2.28087
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发表时间：
2021-01-01
影响因子：
1.1
通讯作者：
Nam, Yunyoung
中科院分区：
文献类型：
--
作者：
Rajinikanth, Venkatesan;Kadry, Seifedine;Nam, Yunyoung
由于人类疾病发生率的增加，对自动化疾病诊断(ADD)系统的需求也增加了。大多数ADD系统都是为了在筛查和决策过程中支持医生。本研究旨在开发一种计算机辅助疾病诊断(CADD)方案，以更准确地将2D MRI切片中的脑肿瘤分类为胶质母细胞瘤/胶质瘤。本研究工作的主要贡献是开发了一个基于卷积神经网络(CNN)的CADD系统。所提出的CADD框架包括以下几个阶段：(I)图像采集和大小调整；(Ii)基于VGG-UNET的自动肿瘤分割；(Iv)基于VGG16网络的深度特征提取；(V)手工特征提取；(Vi)基于萤火虫算法的最佳特征选择；以及(Vii)序列特征连接和二值分类。通过使用基准实现的调查以及临床收集的脑MRI切片，证实了执行CADD的优点。在这项工作中，使用众所周知的分类器实现了一个具有10倍交叉验证的二进制分类，并且使用支持向量机-CUBLE(准确率为98%)获得了更好的结果。这一结果证实了CNN辅助分割和分类的结合有助于提高疾病检测的准确性。
Due to the increased disease occurrence rates in humans, the need for the Automated Disease Diagnosis (ADD) systems is also raised. Most of the ADD systems are proposed to support the doctor during the screening and decision making process. This research aims at developing a Computer Aided Disease Diagnosis (CADD) scheme to categorize the brain tumour of 2D MRI slices into Glioblastoma/Glioma class with better accuracy. The main contribution of this research work is to develop a CADD system with Convolutional-Neural-Network (CNN) supported segmentation and classification. The proposed CADD framework consist of the following phases; (i) Image collection and resizing, (ii) Automated tumour segmentation using VGG-UNet, (iv) Deep-feature extraction using VGG16 network, (v) Handcrafted feature extraction, (vi) Finest feature choice by firefly-algorithm, and (vii) Serial feature concatenation and binary classification. The merit of the executed CADD is confirmed using an investigation realized using the benchmark as well as clinically collected brain MRI slices. In this work, a binary classification with a 10-fold cross validation is implemented using well known classifiers and the results attained with the SVM-Cubic (accuracy >98%) is superior. This result confirms that the combination of CNN assisted segmentation and classification helps to achieve enhanced disease detection accuracy.