Learn to implement and train a U-Net model for melanoma detection using TensorFlow and Keras in this comprehensive tutorial. Begin with data preparation, accessing and preprocessing a substantial dataset of melanoma images and corresponding masks. Explore data augmentation techniques to improve model results. Build a U-Net model using TensorFlow and Keras, then guide through the training process to optimize melanoma detection. Test the pre-trained model on fresh images, generating masks that highlight melanoma regions. Visualize results in real-time, comparing predicted masks with ground truth. Covers U-Net architecture, dataset preparation, model building, training, testing, and result visualization in a step-by-step approach.
Medical Melanoma Detection - TensorFlow U-Net Tutorial
Overview
Syllabus
Intro !!!!!!!
The U-net architecture
Download the dataset and prepare the data
Build The U-net model
Train the model
Test the model with new fresh images
Taught by
Eran Feit
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