Binary Brain Tumor Classification With Semantic Features Using Convolutional Neural Network

In this study, we provide segmentations of brain tumors as semantic features to a simple convolutional neural network (CNN) to improve the classification results. The Siberian Brain Tumor Dataset (SBT) of 1452 magnetic resonance (MR) images of Russian people's brains is used for training, validation, and testing. We preprocess MR images by removing the swelling region surrounding a brain tumor (edema) from the segmentation and eliminating slices that contain less than 163 pixels of tumor. The binary classifier is a simple network of four convolutional layers for feature extractions and two linear layers for classification. The network is trained and validated with 5-fold cross-validation. We train another CNN with similar configuration on images without provided segmentation and compare the testing results. The two networks are evaluated with four metrics: accuracy, sensitivity, specificity, and F1 scores. The classifier trained with segmentation achieve the highest accuracy score of 0.92, sensitivity of 0.934, specificity of 0.91, and F1 score of 0.90 using ensemble approach.