This study explores the application of deep learning techniques in the automated detection and segmentation of brain tumors from MRI scans. We employ several machine learning models, including basic logistic regression, Convolutional Neural Networks (CNNs), and Residual Networks (ResNet) to classify brain tumors effectively. Additionally, we investigate the use of U-Net for semantic segmentation and EfficientDet for anchor-based object detection to enhance the localization and identification of tumors. Our results demonstrate promising improvements in the accuracy and efficiency of brain tumor diagnostics, underscoring the potential of deep learning in medical imaging and its significance in improving clinical outcomes.
@article{arxiv.2510.10250,
title = {MRI Brain Tumor Detection with Computer Vision},
author = {Jack Krolik and Jake Lynn and John Henry Rudden and Dmytro Vremenko},
journal= {arXiv preprint arXiv:2510.10250},
year = {2025}
}
Comments
12 pages, 8 figures, final project report for CS4100 (Machine Learning), Northeastern University, April 2024