Tumor Detection ML Model

🚀 Tumor Detection ML Model

This Tumor Detection ML Model is designed to classify brain tumor images, offering a powerful solution for healthcare image analysis. It utilizes a Convolutional Neural Network (CNN) trained on a labeled dataset of brain tumor MRI images, providing accurate and reliable results.

🚀 Quick Start

You can load the model using the Hugging Face Transformers library:

from transformers import AutoModel
model = AutoModel.from_pretrained("YourUsername/Tumor_detection_ML_Model")

✨ Features

• Designed for classifying brain tumor images using a Convolutional Neural Network (CNN).
• Trained and fine - tuned on a labeled dataset of brain tumor MRI images.

📦 Installation

No specific installation steps were provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import AutoModel
model = AutoModel.from_pretrained("YourUsername/Tumor_detection_ML_Model")

📚 Documentation

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

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.

🔧 Technical Details

The model uses a Convolutional Neural Network (CNN) for image classification. It leverages the TensorFlow/Keras framework, with the Adam optimizer and a learning rate scheduler. The categorical cross - entropy loss function is employed, and data augmentation techniques such as rotation, width/height shift, zoom, and horizontal flipping are used during training. Hyperparameter tuning is carried out using Keras Tuner.

📄 License

This project is licensed under the Apache - 2.0 license.