Classification of brain tumors based on magnetic resonance imaging (MRI) using deep learning models

Abstract

Nowadays, With the advancements in medical care and the improvement in global living standards, there has been a continuous increase in the average life expectancy of people around the world, and the aging problem is constantly aggravated, brain tumors have become a growing global health problem. The project aims to use deep learning models to classify magnetic resonance imaging (MRI) images of brain tumors. By constructing convolutional neural network model, image feature extraction and classification are carried out. Data preprocessing, data enhancement, Adaptive Moment Estimation (Adam) are used to improve the model performance, plot the accuracy curve and generate the confusion matrix, and visualize the classification performance of the model. Possible future tasks may include optimizing the model architecture and expanding the exploration of data augmentation techniques to bolster the model's performance, while also further investigating the model's interpretability to understand the internal mechanisms of the model when making predictions. In addition, the project has great application potential, and the model can be applied to the actual clinical assisted diagnosis system, which has good scalability and application prospect. By using deep learning model and related technologies, the accuracy of brain tumor classification can be improved in clinical diagnosis, and better diagnosis and treatment services can be provided for the majority of brain tumor patients.