Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+datasets: brain-tumor-image-dataset-semantic-segmentation
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+pipeline_tag: image-classification
+tags:
+- brain-tumor
+- image-classification
+- keras
+- tensorflow
+- cnn
+- mri
+- healthcare
+---
+
+# Tumor Detection ML Model
+
+## Model Description
+This model is designed to classify brain tumor images using a Convolutional Neural Network (CNN). It has been trained and fine-tuned on a labeled dataset of brain tumor MRI images.
+
+## Training Details
+- **Framework:** TensorFlow/Keras
+- **Optimizer:** Adam with a learning rate scheduler
+- **Loss Function:** Categorical Crossentropy
+- **Data Augmentation:** Includes rotation, width/height shift, zoom, and horizontal flipping.
+- **Hyperparameter Tuning:** Performed using Keras Tuner.
+
+## Metrics
+The following metrics were used to evaluate the model's performance:
+- **Accuracy:** Measures the overall correctness of predictions.
+- **F1 Score:** Balances precision and recall.
+- **Precision:** Indicates the proportion of true positives among positive predictions.
+- **Recall:** Indicates the proportion of true positives among all actual positives.
+
+## Usage
+You can load the model using the Hugging Face Transformers library:
+```python
+from transformers import AutoModel
+model = AutoModel.from_pretrained("YourUsername/Tumor_detection_ML_Model")