Create README.MD

# Brain Tumor Classification using VGG16 (Colorized MRI)

This repository contains a PyTorch-based VGG16 transfer learning model for automated brain tumor classification from MRI images. The model is trained on enhanced colorized MRI scans and classifies images into three tumor categories: Glioma, Meningioma, and Pituitary tumors.

---

## 🧠 Model Overview

- **Architecture:** VGG16 (Transfer Learning)
- **Framework:** PyTorch
- **Task:** Image Classification
- **Classes:** 3 (Glioma, Meningioma, Pituitary)
- **Input Type:** Colorized MRI images
- **Best Test Accuracy:** **88.70%**
- **Device Used:** GPU (CUDA)

---

## 📊 Dataset Details

- **Dataset Type:** Enhanced Colorized MRI Images
- **Total Samples:** 3,600 images
- **Class Distribution:**
- Glioma
- Meningioma
- Pituitary

- **Data Split:**
- Training: 70%
- Validation: 15%
- Testing: 15%

> Note: The dataset is preprocessed and not hosted publicly on Hugging Face.

---

## ⚙️ Training & Methodology

- Pre-trained VGG16 backbone
- Early convolutional layers frozen
- Custom fully connected classifier
- Image preprocessing with **CLAHE contrast enhancement**
- Data augmentation (rotation, zoom, shift, brightness)
- Optimized training with early stopping and learning rate scheduling

---

## 📈 Performance

**Overall Test Accuracy:** 88.70%

The model demonstrates stable performance across all three tumor classes and serves as a strong comparative baseline against grayscale MRI models.

---

## 🚀 Intended Use

- Research and academic purposes
- Comparative analysis of colorized vs grayscale MRI-based models
- Educational demonstrations of transfer learning in medical imaging

---

## ⚠️ Disclaimer

This model is **NOT a medical diagnostic tool**.
It is intended strictly for **research and educational use**.
Always consult certified medical professionals for clinical diagnosis and treatment.

---

## 🛠 Frameworks & Libraries

- PyTorch
- Torchvision
- OpenCV
- NumPy

---

## 👤 Author

**Prashant Parwani**
Research Project on Brain Tumor Detection using Deep Learning

README.md

@@ -0,0 +1,57 @@
+---
+license: mit
+library_name: pytorch
+pipeline_tag: image-classification
+base_model: vgg16
+metrics:
+- accuracy
+tags:
+- brain-tumor
+- medical-imaging
+- mri
+- vgg16
+- transfer-learning
+- colorized-images
+- pytorch
+- image-classification
+language:
+- en
+---
+
+# Brain Tumor Classification using VGG16 (Colorized MRI)
+
+This repository contains a **VGG16 transfer learning model trained on enhanced colorized MRI images** for automated brain tumor classification.
+
+## 🧠 Tumor Classes
+- Glioma  
+- Meningioma  
+- Pituitary  
+
+## 📊 Model Performance
+- **Test Accuracy:** **88.70%**
+- **Framework:** PyTorch
+- **Architecture:** VGG16 (Transfer Learning)
+- **Pre-trained on:** ImageNet
+- **Input Size:** 224×224 RGB
+- **Number of Classes:** 3
+
+## 🎨 Colorization Strategy
+MRI images were enhanced using **CLAHE** and converted into multiple colormap representations to study the impact of color information on classification performance.
+
+## 🏆 Best Model Checkpoint
+represents the **best-performing checkpoint**, saved at peak validation accuracy.
+
+## 🔬 Training Highlights
+- Transfer learning with frozen convolution layers
+- Fine-tuned classifier head
+- Data augmentation
+- Stratified train/validation/test split (70/15/15)
+- Early stopping and learning rate scheduling
+
+## ⚠️ Disclaimer
+This model is intended **strictly for research and educational purposes** and must not be used for clinical diagnosis or treatment planning.
+
+## 👤 Author
+**Prashant Parwani**
+
+The uploaded file: