Transfer Learning for Image Classification

Build a ResNet 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 ResNet deep learning architecture with basic functional components in Keras • train ResNet model hyperparameters on two different types of medical image datasets (X-ray, CT) • tune ResNet model 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 ResNet deep learning model from scratch to analyze medical imagery. A ResNet is a deep neural network model which uses "Residual blocks" and "skip connections" to reduce the need for very deep networks while still achieving high accuracy. 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 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.

  • Building a ResNet deep learning architecture with basic functional components in Keras
  • Using custom image data generators in Keras
  • Using the DICOM data format for training and test images
  • Deploying ResNet model for training on DICOM images
  • Training ResNet model hyperparameters on two different types of medical image datasets (X-ray, CT)
  • Tuning ResNet model 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 and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
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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