Build a VGG16 Model

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prerequisites
intermediate Python • basics of deep learning • basics of Keras and OpenCV
skills learned
build a VGG16 deep learning architecture in Keras • use custom image data generators in Keras • train VGG16 model on two different types of medical image datasets (X-ray, CT) • tune VGG16 model hyperparameters to improve performance
Anuradha Kar
1 week · 4-6 hours per week · INTERMEDIATE
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Look inside
In this liveProject, you’ll build a VGG16 deep learning model from scratch to analyze medical imagery. A VGG16 is a deep convolutional network model which has shown to achieve high accuracy in image based pattern recognition tasks. 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.

project author

Anuradha Kar
Anuradha Kar is a researcher at the Institut Pasteur in Paris, working on deep learning applications in drug discovery. Before this, she worked at the Paris Brain Institute on applying attention-based deep learning models to understanding the evolution of Alzheimer's disease and at École normale supérieure de Lyon in France on deep learning-based analysis of 3D bio-image datasets. She has a Ph.D. in electrical engineering from the National University of Ireland, Galway. In 2021, she published a liveProject series with Manning Publications titled Transfer Learning for Dicom Image Classification.

prerequisites

This liveProject is for intermediate Python programmers. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Intermediate Python 3.x and Jupyter notebooks
  • Basics of Keras and OpenCV
TECHNIQUES
  • Basics of deep learning and image classification

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