This tutorial provides a step-by-step guide on how to implement and train a U-Net model for Melanoma detection using TensorFlow/Keras.
🔍 What You’ll Learn 🔍:
Data Preparation: We’ll begin by showing you how to access and preprocess a substantial dataset of Melanoma images and corresponding masks.
Data Augmentation: Discover the techniques to augment your dataset. It will increase and improve your model’s results Model Building: Build a U-Net, and learn how to construct the model using TensorFlow and Keras.
Model Training: We’ll guide you through the training process, optimizing your model to distinguish Melanoma from non-Melanoma skin lesions.
Testing and Evaluation: Run the pre-trained model on a new fresh images . Explore how to generate masks that highlight Melanoma regions within the images.
Visualizing Results: See the results in real-time as we compare predicted masks with actual ground truth masks.
Check out our tutorial here : https://youtu.be/P7DnY0Prb2U&list=UULFTiWJJhaH6BviSWKLJUM9sg
Link for the code : https://ko-fi.com/s/901f79116c
If you are interested in learning modern Computer Vision course with deep dive with TensorFlow , Keras and Pytorch , you can find it here : http://bit.ly/3HeDy1V
Perfect course for every computer vision enthusiastic
Before we continue , I actually recommend this book for deep learning based on Tensorflow and Keras : https://amzn.to/3STWZ2N
You can find more tutorials, and join my newsletter here : https://eranfeit.net/
Enjoy
Eran
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