In this liveProject, you’ll build a ResNet deep learning model from scratch to analyze medical imagery. A ResNet is a deep neural network model which uses "Residual blocks" and "skip connections" to reduce the need for very deep networks while still achieving high accuracy. You’ll then train your model on X-ray and CT datasets, and plot validation loss, and accuracies vs. epochs. You’ll build an important familiarity with the functional blocks of a DL model, how data must be formatted, and which layers to use to solve your problems.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
This liveProject is for intermediate Python programmers. To begin this liveProject, you will need to be familiar with:
- Intermediate Python 3.x and Jupyter Notebook
- Basics of Keras and OpenCV
- Basics of deep learning and image classification
you will learn
In this liveProject, you’ll gain familiarity with medical image datasets and build deep neural networks to analyze them.
- Building a ResNet deep learning architecture with basic functional components in Keras
- Using custom image data generators in Keras
- Using the DICOM data format for training and test images
- Deploying ResNet model for training on DICOM images
- Training ResNet model hyperparameters on two different types of medical image datasets (X-ray, CT)
- Tuning ResNet model to improve performance