Create README.md

README.md

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+---
+license: mit
+datasets:
+- masoudnickparvar/brain-tumor-mri-dataset
+metrics:
+- accuracy
+pipeline_tag: image-classification
+library_name: keras
+tags:
+- cnn
+- keras
+- brain-tumor
+- medical-imaging
+- tensor-flow
+- image-classification
+language:
+- en
+---
+
+Brain Tumor Detection CNN Model
+
+This model was trained using a Convolutional Neural Network (CNN) to classify brain MRI images as either having a tumor or not. It uses Keras with TensorFlow backend and was trained on the publicly available [Brain Tumor MRI Dataset](https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset) from Kaggle.
+ Dataset
+
+The dataset contains 3,762 T1-weighted contrast-enhanced MRI images, labeled as:
+
+- **Yes** – Images with a brain tumor
+- **No** – Images without a brain tumor
+
+The data is balanced and preprocessed into two folders: `yes/` and `no/`.
+
+Train Accuracy: ~98% 
+Validation Accuracy: ~96% 
+
+## 🧠 Model Architecture
+
+- Type: CNN
+- Framework: Keras (TensorFlow backend)
+- Input shape: `(150, 150, 3)`
+- Final Activation: `sigmoid`
+- Loss: `binary_crossentropy`
+- Optimizer: `Adam`
+
+Example (simplified):
+
+```python
+model = Sequential([
+    Conv2D(32, (3,3), activation='relu', input_shape=(150, 150, 3)),
+    MaxPooling2D(2,2),
+    Conv2D(64, (3,3), activation='relu'),
+    MaxPooling2D(2,2),
+    Flatten(),
+    Dense(128, activation='relu'),
+    Dense(1, activation='sigmoid')
+])