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Transfer Learning for Image Classification

Build a VGG16 Model you own this product

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

project author

Anuradha Kar
Anuradha Kar is a Postdoctoral researcher at École normale supérieure de Lyon, and works in collaboration with the research institutes INRAE and INRIA in France. Her current research is on the application of deep learning algorithms for deriving quantitative information from microscopy image datasets. This is used by biologists to analyze cellular developmental processes in plants and animals. She has a PhD in electrical engineering from the National University of Ireland, Galway. Her research centers on vision sensors, artificial intelligence and computer vision. She has published on deep learning, human-computer interactions and sensor evaluation techniques.

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

    features

    Self-paced
    You choose the schedule and decide how much time to invest as you build your project.
    Project roadmap
    Each project is divided into several achievable steps.
    Get Help
    While within the liveProject platform, get help from other participants.
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