Transfer Learning for Image Classification

Build a VGG16 Model you own this product

This project is part of the liveProject series Transfer Learning for Dicom Image Classification
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.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

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

    • you will learn

    In this liveProject, you’ll gain familiarity with medical image datasets and build deep neural networks to analyze them.

    • Building a VGG16 deep learning architecture with basic functional components in Keras
    • Using custom image data generators in Keras
    • DICOM data format for training and test images
    • Deploying VGG16 model for training on DICOM images
    • Training VGG16 model on two different types of medical image datasets (X-ray, CT)
    • Tuning VGG16 model hyperparameters to improve performance

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    Compare with others
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    book resources
    Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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