Using deep learning-based classification methods for interpreting brain MRI images for tumor diagnosis

The classification of brain tumors is very important for evaluating and diagnosing the type of tumors and making decisions for treatment according to the stages of disease progression. Many imaging techniques are used to diagnose brain tumors. However, the MRI method is superior compared to other methods due to better image quality and not relying on ionizing radiation. It is obvious that the more accurate the interpretation is, the more it will help the treatment process, and for this purpose, image classification methods that are widely used in remote sensing can be used. Deep learning is a sub-branch of machine learning, and in recent years, it has had a remarkable performance, especially in the topics of image classification and segmentation. In this article, a deep learning model based on a convolutional neural network is proposed to classify different types of brain tumor using a dataset that classifies tumors into meningioma, glioma, and pituitary. MRI imaging methods have different protocols, in this research, the images obtained based on the T1 protocol with a total of 3064 images, which include the images of 233 patients, were used. With the proposed network structure, the overall accuracy of 97.41% was obtained for the data set. The research results show the ability of the model for brain tumor classification purposes.