Brain Tumor Detection using the Concept of Convolutional Neural Network

Abstract

This study explores the application of a Convolutional Neural Network(CNN) for the detection and classification of brain tumors using Magnetic Resonance Imaging(MRI) scans. The datasets for this research are sourced from Kaggle. The CNN model receives the training and test accuracy of 0.9876 and 0.947 respectively. Model performance was assessed using evaluation metrics such as the confusion matrix, F1 score, precision and recall. The training process was carried out over 11 epochs, with a batch size of 16 and a learning rate of 0.001. The outcome of this study display CNN’s efficiency in medical imaging analysis, which contributes to the diagnosis accuracy and progress in computational healthcare.