https://github.com/nour-zayed/brain-tumor-efficientnetb3
Brain Tumor Detection using EfficientNetB3-based Deep Learning model. The project leverages transfer learning on MRI brain scan images to classify and detect brain tumors with high accuracy. Includes full workflow: data preprocessing, image augmentation, model building, evaluation, and deployment.
https://github.com/nour-zayed/brain-tumor-efficientnetb3
batchnormalization deep-learning efficientnet flatten imagedatagenerator keras maxpooling2d python regularizers streamlit tensorflow
Last synced: 1 day ago
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Brain Tumor Detection using EfficientNetB3-based Deep Learning model. The project leverages transfer learning on MRI brain scan images to classify and detect brain tumors with high accuracy. Includes full workflow: data preprocessing, image augmentation, model building, evaluation, and deployment.
- Host: GitHub
- URL: https://github.com/nour-zayed/brain-tumor-efficientnetb3
- Owner: Nour-Zayed
- Created: 2025-04-10T19:40:17.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-22T17:38:26.000Z (about 1 year ago)
- Last Synced: 2025-10-24T08:28:54.333Z (9 months ago)
- Topics: batchnormalization, deep-learning, efficientnet, flatten, imagedatagenerator, keras, maxpooling2d, python, regularizers, streamlit, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 2.76 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 🧠 Brain Tumor Classification using EfficientNetB3
📌 **Overview**
This project presents a high-performance deep learning pipeline for automated brain tumor classification using MRI images.
It employs EfficientNetB3, a cutting-edge convolutional neural network (CNN) architecture,
fine-tuned to accurately distinguish between various types of brain tumors.
Our goal is to provide a fast, reliable, and scalable solution that can assist medical professionals in making informed diagnostic decisions,
reducing manual workload, and improving early detection rates.
🚀**Highlights**
🎯 High-accuracy multi-class classification of brain tumors.
⚡ Powered by EfficientNetB3, known for its efficiency and superior performance.
🧹 Built-in data preprocessing and augmentation to enhance generalization.
📊 Rich metrics visualization and confusion matrix for evaluation insights.
🔁 Modular design for seamless training, evaluation, and deployment.
🌐 Includes an interactive **Streamlit web app**.
🧠 **Tumor Classes**
The model classifies MRI brain scans into the following four categories:
Glioma Tumor
Meningioma Tumor
Pituitary Tumor
No Tumor
📈 **Model Performance**
Metric Score
Accuracy ✅ 97%+
Precision ✅ High
Recall ✅ High
F1-Score ✅ Balanced
🙌 **Contributing**
We welcome all kinds of contributions! Whether it's bug fixes, suggestions, or adding new features — feel free to fork the repo and submit a pull request.
