Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+tags:
+  - brain-tumor
+  - medical-imaging
+  - multimodal
+  - pytorch
+  - ct
+  - mri
+  - streamlit
+  - classification
+model-index:
+  - name: MultiModal Brain Tumor Classifier
+    results: []
+---
+
+# 🧠 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](https://github.com/lukmanaj/CTMRI-Net)).
+
+## 🚀 Usage
+
+```python
+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
+
+| Epoch | Train Loss | Accuracy |
+|-------|------------|----------|
+| 1     | 0.1552     | 94.82%   |
+| 5     | 0.0368     | 98.78%   |
+
+> **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](https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-multimodal-image-ct-and-mri)
+