Medical Polyp Image Segmentation with U-Net and TensorFlow Keras

This tutorial provides a step-by-step guide on how to implement and train a U-Net model for polyp segmentation using TensorFlow/Keras.

The tutorial is divided into four parts:

Part 1: Data Preprocessing and Preparation In this part, you load and preprocess the polyp dataset, including resizing images and masks, converting masks to binary format, and splitting the data into training, validation, and testing sets.

Part 2: U-Net Model Architecture This part defines the U-Net model architecture using Keras. It includes building blocks for convolutional layers, constructing the encoder and decoder parts of the U-Net, and defining the final output layer.

Part 3: Model Training Here, you load the preprocessed data and train the U-Net model. You compile the model, define training parameters like learning rate and batch size, and use callbacks for model checkpointing, learning rate reduction, and early stopping. The training history is also visualized.

Part 4: Model Evaluation and Inference The final part demonstrates how to load the trained model, perform inference on test data, and visualize the predicted segmentation masks.

Code for this video: https://github.com/feitgemel/TensorFlowProjects/tree/master/Unet-Projects/Polyp%20Segmentation%20-%20Binary

More relevant content in this playlist : https://youtube.com/playlist?list=PLdkryDe59y4Ze9_12JhWu3cs-lOGYwYeD

00:00 Intro !!!!!!!
00:44 The U-net architecture
03:58 Download the dataset and prepare the data
20:30 Build The U-net model
28:00 Train the model 
43:14 Test the model with new fresh images

 ~~~~~~~~~~~~~~~ recommended courses and books ~~~~~~~~~~~~~~~
A perfect course for learning modern Computer Vision with deep dive in TensorFlow , Keras and Pytorch . You can find it here : http://bit.ly/3HeDy1V 
I also recommend this book, https://amzn.to/44GnlLW : "Make Your Own Neural Network - An In-depth Visual Introduction For Beginners ".
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