Welcome to our Brain Tumor Detection System, an advanced deep-learning solution engineered to support early tumor identification from MRI scans. Built using the cutting-edge Vision Transformer (ViT) architecture, this system demonstrates the capabilities of modern AI in improving medical imaging analysis, research, and clinical decision support.
This project leverages the powerful google/vit-base-patch16-224-in21k Vision Transformer model, fine-tuned specifically for binary MRI classification. The system distinguishes between:
- Tumor Detected
- No Tumor
with high precision and reliable consistency.
By harnessing ViT’s attention-based feature extraction, the model excels in identifying subtle abnormalities often missed by traditional CNNs.
A clean, responsive, and elegantly designed Streamlit application forms the frontend of this project. The interface provides:
- Effortless MRI image upload
- Smooth preview of the uploaded scan
- Instant prediction with visual emphasis
- Custom-designed CSS for premium user experience
- Educational content for medical awareness
This UI ensures accessibility for students, researchers, and healthcare learners.
Beyond predictions, the application includes a curated sidebar that provides:
- Causes of brain tumors
- Risk factors and medical considerations
- Lifestyle do's and don'ts
- Symptoms and guidance
- General well-being tips
This transforms the system into a holistic educational tool—not just a classifier.
- Runs seamlessly on CPU
- Performs inference in real-time
- Processes all data locally (no cloud storage)
- Ensures complete privacy of sensitive MRI images
The setup is minimal, making it suitable for research projects, demonstrations, and academic submissions.
This project is ideal for:
- Deep Learning and Computer Vision students
- Healthcare AI researchers
- Anyone exploring Vision Transformers
- Individuals learning web deployment using Streamlit
- Academic proof-of-concept demonstrations
The modular structure and clear code make this project easy to extend, retrain, or integrate into larger medical imaging systems.
The goal of this project is to showcase how modern deep-learning architectures like Vision Transformers can be applied to medical imaging tasks with high accuracy and interpretability. While not a replacement for clinical diagnosis, this system highlights the transformative potential of AI in healthcare innovation.