brain-tumor-multimodal

🧠 Brain Tumor Classifier (CT + MRI)

This model uses a multimodal DenseNet architecture to classify brain tumors based on CT and MRI scans. It was trained on paired data with class labels: Healthy or Tumour.

🔬 Model Details

• Architecture: Dual DenseNet201 backbones + fusion classifier
• Input modalities: CT image, MRI image (either or both)
• Output: Binary classification (Healthy, Tumour)
• Framework: PyTorch

📦 Files

• multimodal_brain_tumor_model.pth: Pretrained weights
• Intended to be used with a Streamlit app (see GitHub Repo).

🚀 Usage

from huggingface_hub import hf_hub_download
import torch

path = hf_hub_download("lukmanaj/brain-tumor-multimodal", "multimodal_brain_tumor_model.pth")
model.load_state_dict(torch.load(path))

📊 Training Performance

Note: The model shows signs of overfitting; further validation and augmentation are advised.

🧑‍⚕️ Intended Use

• Designed for educational and research purposes.
• Not certified for clinical or diagnostic use.

📚 Citation

Aliyu, L. (2025). Brain Tumor Classification using Multimodal Deep Learning

🤝 Acknowledgements

• Masoud Nickparvar’s CT+MRI dataset on Kaggle