MRI Brain Tumor Detection with Computer Vision
This work addresses the need for faster and more accurate brain tumor detection in medical imaging, though it appears incremental by applying existing methods to this domain.
This study tackled the problem of automated brain tumor detection and segmentation from MRI scans using deep learning models, achieving promising improvements in accuracy and efficiency for diagnostics.
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.