Brain Tumor Classification Based on Attention Guided Deep Learning Model
Jun Wen, Zheng Liyuan
Abstract
Abstract Cancer is the second leading cause of death worldwide. Brain tumors count for one out of every four cancer deaths. Providing an accurate and timely diagnosis can result in timely treatments. In recent years, the rapid development of image classification has facilitated computer-aided diagnosis. The convolutional neural network (CNN) is one of the most widely used neural network models for classifying images. However, its effectiveness is limited because it cannot accurately identify the focal point of the lesion. This paper proposes a novel brain tumor classification model that integrates an attention mechanism and a multipath network to solve the above issues. An attention mechanism is used to select the critical information belonging to the target region while ignoring irrelevant details. A multipath network assigns the data to multiple channels, before converting each channel and merging the results of all branches. The multipath network is equivalent to grouped convolution, which reduces the complexity. Experimental evaluations on this model using a dataset consisting of 3064 MR images achieved an overall accuracy of 98.61%, which outperforms previous studies on this dataset.