Image classification of brain tumor based on channel attention mechanism

Abstract

The main method of traditional medicine to diagnose brain tumors is that expert doctors read the pictures with naked eyes. Meanwhile, the annotation of this type of medical image data set requires to be done manually by experienced experts, which makes the production cost of labeled medical image data set higher. In view of the above situation, this study firstly used unsupervised learning method Deep Convolution Generative Adversarial Networks (DCGAN) to generate data set expansion on the four types of MRI brain tumor images including glioma, meningioma, pituitary tumor and no tumor. In addition, an improved DenseNet-201 brain tumor classification network model is proposed, which uses the channel attention mechanism SENet as a bypass connecting the Convolutional layer and the Transition layer (or the Transition layer and the Transition layer), and interweaves the classification model on this basis. Optimized the problem of weak resolution of the original model to extract features, thereby accurately capturing the position, shape and texture information of the brain tissue in the MRI image. The model improves the accuracy of the brain MRI image classification, and enhances the generalization ability of the model.