https://github.com/tahirzia-1/brain-tumor-classification-via-deep-learning
An AI model that Classifies between 4 classes of Brain Tumors. Well-established CNN architecture pre-trained on a massive dataset of MRI scans. VGG16 model is used for this task.
https://github.com/tahirzia-1/brain-tumor-classification-via-deep-learning
ai brain braintumorclassification cnn cnn-classification cnn-keras cnn-model ipynb ipynb-jupyter-notebook tumor tumor-detection vgg16
Last synced: 4 months ago
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An AI model that Classifies between 4 classes of Brain Tumors. Well-established CNN architecture pre-trained on a massive dataset of MRI scans. VGG16 model is used for this task.
- Host: GitHub
- URL: https://github.com/tahirzia-1/brain-tumor-classification-via-deep-learning
- Owner: TahirZia-1
- Created: 2024-05-24T16:35:52.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-26T18:23:45.000Z (8 months ago)
- Last Synced: 2025-02-26T19:24:06.963Z (8 months ago)
- Topics: ai, brain, braintumorclassification, cnn, cnn-classification, cnn-keras, cnn-model, ipynb, ipynb-jupyter-notebook, tumor, tumor-detection, vgg16
- Language: Jupyter Notebook
- Homepage:
- Size: 1.19 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Brain Tumor Classification via Deep Learning
## Project Overview
This project implements a brain tumor classification system using deep learning techniques. The model utilizes a pre-trained VGG16 architecture with transfer learning to classify brain MRI scans into four categories:
- Glioma tumor
- Meningioma tumor
- No tumor
- Pituitary tumor
## Dataset
The dataset consists of approximately 4,800 labeled MRI scans divided into pre-defined training and validation sets. The dataset can be downloaded from Kaggle (link to be added).
## Methodology
### Data Preprocessing
- Images are resized to a uniform size
- Pixel values are normalized to the range [0, 1]
- File paths are systematically created using os.path.join for platform-independent access
### Model Architecture
The model leverages the VGG16 architecture with transfer learning:
- The pre-trained VGG16 model serves as a feature extractor
- Initial layers capturing generic image features are kept frozen
- Final layers are replaced with new Dense layers using softmax activation
- The model is adapted for the four-class classification task
### Training Process
- Loss Function: Sparse categorical cross-entropy
- Optimizer: Adam
- Metrics: Accuracy
- Early stopping implemented to prevent overfitting
- TensorBoard visualization utilized for monitoring training
## Implementation Details
### Requirements
- Python 3.x
- TensorFlow/Keras
- NumPy
- Pandas
- Matplotlib (for visualization)
- scikit-learn (for evaluation metrics)
### Code Structure
```
brain-tumor-classification/
├── data/
│ ├── training/
│ └── validation/
├── models/
├── src/
│ ├── preprocess.py
│ ├── model.py
│ ├── train.py
│ └── evaluate.py
└── README.md
```
## Usage
### Setup
```bash
# Clone the repository
git clone https://github.com/username/brain-tumor-classification.git
cd brain-tumor-classification
# Install dependencies
pip install -r requirements.txt
```
### Training
```bash
python src/train.py
```
### Evaluation
```bash
python src/evaluate.py
```
## Results
The model was trained for 12 epochs with a batch size of 32. Performance metrics include:
- Overall accuracy
- Precision, recall, and F1-score for each tumor class
- Test loss to evaluate generalization capability
## Future Work
- Explore different hyperparameter configurations
- Test alternative pre-trained models
- Implement more extensive data augmentation techniques
- Investigate model explainability for clinical applications
## References
1. Saleh, A., Sukaik, R., & Abu-Naser, S. S. (2020). Brain tumor classification using deep learning.
2. Paul, J. S., Plassard, A. J., Landman, B. A., & Fabbri, D. (2017). Deep learning for brain tumor classification.
3. Ari, A., & Hanbay, D. (2018). Deep learning based brain tumor classification and detection system.
4. Díaz-Pernas, F. J., Martínez-Zarzuela, M., Antón-Rodríguez, M., & González-Ortega, D. (2021). A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network.
5. Tandel, G. S., et al. (2019). A review on a deep learning perspective in brain cancer classification.
## Author
Muhammad Tahir Zia
Bachelors Computer Engineering
GIK Institute, Topi, Pakistan
u2021465@giki.edu.pk