Convolutional Neural Network Model for Classification and Prediction of Brain Tumor Based on Magnetic Resonance Imaging

Abstract

Accurate diagnosis of brain tumors is crucial in medicine, requiring high-quality magnetic resonance imaging to correctly identify the type and location of the tumor, which are essential factors for effective treatment. The aim of this study is to develop a convolutional neural network to predict types of brain tumors, applying computer vision techniques and artificial intelligence. The method uses Magnetic Resonance Imaging, and the study applied an experimental approach with a five-layer convolutional neural network on Google Colab, aided by the Keras and TensorFlow libraries. The convolutional neural network demonstrated significant improvements in the precision of predicting types of brain tumors, effectively classifying images and identifying distinctive characteristics of the tumors. The application of convolutional neurais networks in the analysis of brain tumor images is promising, contributing to the advancement of artificial intelligence in medicine and improving the accuracy of diagnosis and treatment of brain tumors.

Keywords: Computer Vision, Convolutional Neural Network, Deep Learning, Magnetic Resonance Imaging.