---
language: en  
license: mit  
tags:  
  - deep-learning  
  - medical-imaging  
  - tumor-detection  
  - MRI  
  - h5  
model-index:  
  - name: MRI_LLM  
    results:  
      - task:  
          type: image-classification  
        dataset:  
          name: Public MRI datasets (Kaggle, NIH, TCIA)  
          type: image  
        metrics:  
          - type: accuracy  
            value: 95.2  
---


# MRI_LLM: Brain, Breast, and Lung Tumor Detection Models

📌 **Author**: Vijayendher Gatla (@wizaye)  
📌 **Repository**: [https://huggingface.co/wizaye/MRI_LLM](https://huggingface.co/wizaye/MRI_LLM)  
📌 **License**: MIT  
📌 **Tags**: `deep-learning`, `medical-imaging`, `tumor-detection`, `MRI`, `h5`  

---

## **Model Overview**  
The **MRI_LLM** repository contains three deep learning models trained for **tumor detection** in **brain, breast, and lung MRIs**. These models leverage deep neural networks to assist in the automated diagnosis of tumors from medical imaging data.  

### **Models Included**  
- **Brain Tumor Model (`brain_model.h5`)**: Detects tumors in MRI brain scans.  
- **Breast Tumor Model (`breast_tumor.h5`)**: Identifies malignant and benign breast tumors.  
- **Lung Tumor Model (`lung_tumor.h5`)**: Predicts lung tumors using CT/MRI scans.  

---

## **Intended Use**  
These models are designed for **research and educational purposes**. They can be used for:  
✅ Assisting radiologists in medical image analysis  
✅ Experimenting with deep learning in healthcare  
✅ Further fine-tuning on custom datasets  

**⚠️ Disclaimer:** These models are **not** FDA/CE-approved and should not be used for clinical diagnosis.  

---

## **Model Architecture**  
Each model is based on **Convolutional Neural Networks (CNNs)**, specifically optimized for medical image classification. The architecture includes:  
- **Feature extraction** layers for capturing patterns in MRI scans  
- **Fully connected** layers for classification  
- **Softmax/Sigmoid activation** depending on the number of classes  

---

## **Dataset**  
- The models were trained on **publicly available MRI datasets** (e.g., Kaggle, NIH, TCIA).  
- Data preprocessing included **normalization, augmentation, and resizing**.  
- If you are using these models, make sure to verify dataset compatibility.  

---

## **How to Use**  

### **Load the Model**  
```python
from tensorflow.keras.models import load_model

# Load Brain Tumor Model
model = load_model("brain_model.h5")

# Predict on new images
import numpy as np
from tensorflow.keras.preprocessing import image

img_path = "sample_mri.jpg"
img = image.load_img(img_path, target_size=(224, 224))
img_array = image.img_to_array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)

prediction = model.predict(img_array)
print("Tumor Detected" if prediction > 0.5 else "No Tumor")
```

---

## **Performance Metrics**  
| Model | Accuracy | Precision | Recall |  
|--------|----------|------------|----------|  
| **Brain Tumor** | 95.2% | 94.8% | 96.1% |  
| **Breast Tumor** | 93.5% | 92.7% | 94.3% |  
| **Lung Tumor** | 96.1% | 95.9% | 96.8% |  

📌 Trained using **TensorFlow/Keras** on NVIDIA GPUs.  

---

## **Limitations & Future Work**  
🔹 Limited dataset coverage—may not generalize to all MRI variations.  
🔹 Possible false positives/negatives in real-world cases.  
🔹 Can be improved with **transfer learning** on hospital-specific datasets.  

---

## **Citation**  
If you use this model, please cite:  
```bibtex
@misc{MRI_LLM,
  author = {Vijayendher Gatla},
  title = {MRI-Based Tumor Detection Models},
  year = {2025},
  url = {https://huggingface.co/wizaye/MRI_LLM}
}
```