Smart Brain Tumor Detection and Severity Monitoring using Deep Neural Networks

Abstract

Rapid and accurate diagnosis of brain tumors is essential for planning appropriate treatment and improving patient survival. This paper presents a DL-based automated system as a means of categorizing brain tumors and staging using Magnetic Resonance Imaging (MRI) data implemented in MATLAB. The proposed approach employs a transfer learning strategy using a pre-trained ResNet-50 convolutional neural network to extract discriminative characteristics from brain MRI images. Before grouping, comprehensive pre-processing methods such as scaling, normalization, denoising, and data augmentation are used. The technique estimates tumor severity levels while categorizing MRI images into meningioma, glioma, pituitary tumor, or normal categories. Standard measures such as F1-score, confusion matrix, accuracy, precision, recall, and Receiver Operating Characteristic (ROC) analysis are used to assess performance. Experiments show that the suggested model outperforms traditional techniques and delivers dependable staging performance and high classification accuracy, confirming its applicability for computer-aided clinical decision support systems.