README.md

README.md

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+---
+license: mit
+base_model: vgg16
+library_name: pytorch
+pipeline_tag: image-classification
+metrics:
+- accuracy
+tags:
+- brain-tumor
+- medical-imaging
+- mri
+- vgg16
+- transfer-learning
+- grayscale-images
+- pytorch
+- image-classification
+language:
+- en
+---
+
+# Brain Tumor Classification using VGG16 (Grayscale MRI)
+
+This repository contains a **VGG16 transfer learning model trained on enhanced grayscale MRI images** for automated brain tumor classification.
+
+## 🧠 Tumor Classes
+- Glioma  
+- Meningioma  
+- Pituitary  
+
+## 📊 Model Performance
+- **Test Accuracy:** **93.52%**
+- **Framework:** PyTorch
+- **Architecture:** VGG16 (Transfer Learning)
+- **Pre-trained on:** ImageNet
+- **Input Size:** 224×224 RGB (grayscale replicated to 3 channels)
+- **Number of Classes:** 3
+
+## 🌑 Grayscale Strategy
+MRI images were processed in **grayscale format** and enhanced using **CLAHE contrast enhancement** to preserve structural and intensity-based features critical for medical diagnosis.
+
+This approach demonstrated **superior performance** compared to colorized representations.
+
+## 🏆 Best Model Checkpoint
+The uploaded file represents the **best-performing checkpoint**, saved at the highest validation accuracy during training.
+
+## 🔬 Training Highlights
+- Transfer learning with frozen convolution layers
+- Fine-tuned classifier head
+- CLAHE-based preprocessing
+- 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**