Model Card created

README.md

@@ -0,0 +1,80 @@
+---
+license: mit
+datasets:
+- brain-mri-dataset
+metrics:
+- accuracy
+- auc
+model-index:
+- name: CNN Brain Tumor Classifier
+  results:
+    - task:
+        type: image-classification
+      dataset:
+        name: Brain MRI Dataset
+        type: brain-mri-dataset
+      metrics:
+        - name: Accuracy
+          type: accuracy
+          value: 0.90
+        - name: AUC
+          type: auc
+          value: 0.90
+---
+
+# 🧠 CNN Brain Tumor Classifier
+
+## Model Description
+This repository contains a Convolutional Neural Network (CNN) built with **TensorFlow/Keras** for classifying brain MRI scans.  
+The model can distinguish between three types of brain tumors and healthy scans.  
+
+⚠️ **Disclaimer**: This model is provided for **educational and research purposes only**.  
+It is **not a medical diagnostic tool** and should not be used in clinical practice.  
+
+---
+
+## Classes
+The model predicts one of the following four categories:
+- **Glioma**
+- **Meningioma**
+- **Pituitary tumor**
+- **No tumor** (healthy)
+
+---
+
+## Training Details
+- **Framework**: TensorFlow / Keras  
+- **Architecture**: Custom CNN  
+- **Input size**: 224 × 224 RGB MRI images  
+- **Loss function**: `categorical_crossentropy`  
+- **Optimizer**: `Adam`  
+- **Epochs**: 10  
+- **Metrics**: Accuracy, AUC  
+
+---
+
+## Dataset
+- **Source**: Public brain MRI dataset (glioma, meningioma, pituitary, no tumor)  
+- **Preprocessing**:
+  - Images resized to 224 × 224  
+  - Normalized to [0, 1] range  
+  - Augmentation (rotation, flipping, zoom) applied during training  
+
+---
+
+## Evaluation Results
+| Metric              | Value     |
+|---------------------|-----------|
+| Training Accuracy   | ~96%      |
+| Validation Accuracy | ~85–90%   |
+| AUC (training)      | ~0.90     |
+
+*(values may vary depending on train/validation split)*  
+
+---
+
+## Usage
+
+### Installation
+```bash
+pip install tensorflow huggingface_hub