MRI Brain Tumor Detection with Computer Vision
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Abstract (arXiv)
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.
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References · 8
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,020
- 2018A Tutorial on Bayesian Optimizationcited by 744
- 2020Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNetscited by 167
- 2023Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Unioncited by 17
- 2022Cross validation for model selection: a primer with examples from ecologycited by 1
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