Brain-Tumor-Detection and Brain-Tumor-Classification-Using-Deep-Learning-Algorithms
These are competitors offering alternative implementations of the same core task—both apply deep learning (CNN-based approaches) to detect brain tumors from medical imaging—with the primary difference being that B additionally attempts tumor classification and localization while A focuses on detection alone.
About Brain-Tumor-Detection
MohamedAliHabib/Brain-Tumor-Detection
Brain Tumor Detection Using Convolutional Neural Networks.
This project helps medical professionals or researchers quickly identify potential brain tumors from MRI images. You input brain MRI scans, and the system outputs a classification indicating whether a tumor is present, along with the confidence of that prediction. It is designed for use by radiologists, neurologists, or medical image analysts.
About Brain-Tumor-Classification-Using-Deep-Learning-Algorithms
SartajBhuvaji/Brain-Tumor-Classification-Using-Deep-Learning-Algorithms
To Detect and Classify Brain Tumors using CNN and ANN as an asset of Deep Learning and to examine the position of the tumor.
Implements comparative benchmarking across ANN, CNN, and transfer learning (VGG16) architectures on 3,260 augmented MRI images to classify tumors into three categories (benign, malignant, pituitary), achieving 94% validation accuracy with VGG16. Leverages T1-weighted contrast-enhanced MRI data and includes a web interface alongside the core classification models. Integrates with Kaggle datasets and HuggingFace for dataset distribution, with extensible contribution framework for community-submitted architectures.
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