Brain tumor image segmentation based on convolution neural network

Automatic segmentation and early diagnosis of brain tumor is a challenging problem in computer vision and it can provide possibility for pre-operative planning, and solve the problem such as low accurateness and time-consuming in traditional manual segmentation. Under the mentioned problems above, this paper put forward a new method: Based on traditional convolutional neural networks (CNNs), a new architecture model is proposed for automatic brain tumor segmentation, which combines multi-modality images. The newly designed CNNs model automatically learns useful features from multi-modality images to combine multi-modality information. Experiment results show that the proposed model is more accurate than traditional methods and can provide reliable information for clinic treatments.