---
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")