Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists
Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists
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DOI：
10.3390/diagnostics10080565
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发表时间：
2020-08-01
期刊：
影响因子：
3.6
通讯作者：
Bukhari, Syed Ahmad Chan
中科院分区：
文献类型：
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作者：
Khan, Muhammad Attique;Ashraf, Imran;Bukhari, Syed Ahmad Chan
对于放射科医生来说，人工识别脑肿瘤是一个容易出错和繁琐的过程，因此，采用自动化系统至关重要。二分类过程，如恶性或良性是相对微不足道的；而多模式脑肿瘤分类(T1、T2、T1CE和FLAIR)对放射科医生来说是一项具有挑战性的任务。在这里，我们提出了一种基于深度学习的自动多模式分类方法，用于脑肿瘤类型的分类。该方法由五个核心步骤组成。第一步，采用基于边缘的直方图均衡化和离散余弦变换(DCT)进行线性对比度拉伸。第二步，进行深度学习特征提取。利用转移学习方法，利用两个预先训练好的卷积神经网络模型VGG16和VGG19进行特征提取。在第三步中，结合极限学习机(ELM)实现了一种基于相关熵的联合学习方法来选择最佳特征。第四步，将基于偏最小二乘的稳健协变特征融合到一个矩阵中。组合的矩阵被馈送到ELM进行最终分类。该方法在BRATS数据集上得到了验证，对BraTs2015、BraTs2017和BraTs2018分别达到了97.8%、96.9%和92.5%的准确率。
Manual identification of brain tumors is an error-prone and tedious process for radiologists; therefore, it is crucial to adopt an automated system. The binary classification process, such as malignant or benign is relatively trivial; whereas, the multimodal brain tumors classification (T1, T2, T1CE, and Flair) is a challenging task for radiologists. Here, we present an automated multimodal classification method using deep learning for brain tumor type classification. The proposed method consists of five core steps. In the first step, the linear contrast stretching is employed using edge-based histogram equalization and discrete cosine transform (DCT). In the second step, deep learning feature extraction is performed. By utilizing transfer learning, two pre-trained convolutional neural network (CNN) models, namely VGG16 and VGG19, were used for feature extraction. In the third step, a correntropy-based joint learning approach was implemented along with the extreme learning machine (ELM) for the selection of best features. In the fourth step, the partial least square (PLS)-based robust covariant features were fused in one matrix. The combined matrix was fed to ELM for final classification. The proposed method was validated on the BraTS datasets and an accuracy of 97.8%, 96.9%, 92.5% for BraTs2015, BraTs2017, and BraTs2018, respectively, was achieved.