We have developed six convolutional neural network (CNN) models for finding optimal brain tumor detection system on high‐grade glioma and low‐grade glioma lesions from voluminous magnetic resonance imaging human brain scans. Glioma is the most common form of brain tumor. The models are constructed based on the different combinations and settings of hyperparameters with conventional CNN architecture. The six models are two layers with five epochs, five layers with dropout, five layers with stopping criteria (FLSC), FLSC and dropout (FLSCD), FLSC and batch normalization (FLSCBN), and FLSCBN and dropout. The models were trained and tested with BraTS2013 and whole brain atlas data sets. Among them, FLSCBN model yielded the best classification results for brain tumor detection. Experimental results revealed that our deep learning approach was better than the conventional state‐of‐art methods.

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基于深度卷积神经网络的脑胶质瘤自动检测系统的开发

我们开发了六个卷积神经网络 (CNN) 模型，用于从大量磁共振成像人脑扫描中寻找针对高级别神经胶质瘤和低级别神经胶质瘤病变的最佳脑肿瘤检测系统。胶质瘤是最常见的脑肿瘤形式。这些模型是基于超参数与传统 CNN 架构的不同组合和设置构建的。这六个模型是两层有五个 epoch，五层有 dropout，五层有停止标准（FLSC），FLSC 和 dropout（FLSCD），FLSC 和批量归一化（FLSCBN），以及 FLSCBN 和 dropout。这些模型使用 BraTS2013 和全脑图谱数据集进行训练和测试。其中，FLSCBN模型对脑肿瘤检测产生了最好的分类结果。