Unet Tutorial | Medical Polyp Image Segmentation with TensorFlow Keras and U-net

Image segmentation is a crucial task in medical image analysis, as it allows for the precise delineation of anatomical structures and lesions from medical images. In this tutorial, we will explore how to perform medical polyp image segmentation using the U-net architecture with TensorFlow Keras.

What is U-net?

U-net is a convolutional neural network architecture that is specifically designed for biomedical image segmentation. It consists of a contracting path for capturing context and a symmetric expanding path for precise localization. U-net has been widely used in medical image analysis and has shown great performance in various segmentation tasks.

Setting Up the Environment

Before we begin, make sure you have TensorFlow and Keras installed in your environment. You can install them using pip:

pip install tensorflow keras

Data Preparation

For this tutorial, we will use a dataset of medical polyp images. You can download the dataset from a public source or use your own dataset. Make sure to preprocess the images and corresponding ground truth masks before training the U-net model.

Building the U-net Model

We will use TensorFlow Keras to build the U-net model. Define the architecture of the U-net model with the contracting and expanding paths, and compile the model with appropriate loss and optimization functions.

Training the Model

Once the model is built, you can train it using the preprocessed dataset. Split the dataset into training and validation sets, and use the fit() function to train the model. Monitor the training progress using metrics such as loss and accuracy.

Testing and Evaluation

After training, evaluate the performance of the model on a separate test set. Use metrics such as dice coefficient and intersection over union (IoU) to assess the quality of the segmentation masks generated by the U-net model.

Conclusion

In this tutorial, we have learned how to perform medical polyp image segmentation using the U-net architecture with TensorFlow Keras. U-net is a powerful tool for biomedical image segmentation and can be applied to various medical image analysis tasks.