https://github.com/niranjanchaudhari0929/detecting-brain-tumor-using-deep-learning-models
Prediction model developed for predicting presence and type of brain tumor using MRI scans of brain.
https://github.com/niranjanchaudhari0929/detecting-brain-tumor-using-deep-learning-models
matplotlib pandas seaborn sklearn tensorflow
Last synced: 3 months ago
JSON representation
Prediction model developed for predicting presence and type of brain tumor using MRI scans of brain.
- Host: GitHub
- URL: https://github.com/niranjanchaudhari0929/detecting-brain-tumor-using-deep-learning-models
- Owner: NiranjanChaudhari0929
- Created: 2024-11-14T15:22:33.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-15T05:06:44.000Z (over 1 year ago)
- Last Synced: 2025-01-18T12:52:24.948Z (over 1 year ago)
- Topics: matplotlib, pandas, seaborn, sklearn, tensorflow
- Language: Python
- Homepage:
- Size: 19.5 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Detecting-Brain-Tumor-using-Deep-learning-models
Our study involves the application of two distinct datasets, each consisting of unique challenges and complexities in brain tumor classification.The first dataset obtained from Kaggle comprises magnetic resonance imaging (MRI) scans of the brain, categorizing samples into those with and without tumors. Whereas the second dataset obtained from Kaggle intro-duces a more deep classification task, including three tumor types: meningioma, glioma, and pituitary tumor. This dataset incorporates MRI scans captured from various angles, contributing to a comprehensive evaluation of the model’s robustness. To compare our model, we leverage popular pre-trained architectures available in the Tensorflow library, such as VGG19 (Visual Geometry Group19), ResNet50 (Residual Network 50), DenseNet121 (Densely Connected Convo-lutional Networks 121), Xception (Extreme Inception), InceptionV3 (InceptionVersion 3), MobileNetV2 (Mobile Network Version 2), and EfficientNetB7 (Effi-
cient Network B7).
## The work in the project is under process, hence the code for our model hasn't been uplaoded yet ##