In this liveProject, you’ll take pretrained VGG16 and ResNet models from the Python Keras library and train them further upon your medical image dataset of X-ray and CT scans. This transfer learning is a highly effective technique for quickly generating reliable machine learning models when you only have a small data set. You’ll experiment with the Keras loss functions to determine which are best for COVID image classification, and check your training and prediction times as a critical parameter of real-world applications.
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.
- Transfer learning for training VGG16 and ResNet models in Keras
- Using custom image data generators in Keras
- Using DICOM data format for training and test images
- Deploying VGG16 and ResNet models for training on DICOM images