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README.md

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+# 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}
+}
+```