Transfer Learning for Image Classification

Transfer Learning 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
use transfer learning for training VGG16 and ResNet models in Keras • deploy VGG16 and ResNet models for training on DICOM images • train VGG16 and ResNet models on two different types of medical image datasets
Anuradha Kar
1 week · 4-6 hours per week · INTERMEDIATE

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Look inside
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.

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 Notebook
  • 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.

  • 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

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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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