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Transfer Learning for Image Classification

Evaluate and Explain DL Models 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
make predictions using deep learning models using Keras • implement Grad-CAM visualization • implement deep learning model performance metrics • create a radar plot to display the performance of multiple models
Anuradha Kar
1 week · 4-6 hours per week · INTERMEDIATE
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liveProject This project is part of the liveProject series Transfer Learning for Dicom Image Classification liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save $10 (33%)
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In this liveProject, you’ll implement model performance metrics that can test the effectiveness of your models. You’ll calculate accuracy, precision, F1 score and recall values from the classification results for an existing model, and then estimate the ROC curve and AUC value. Finally, you’ll create a Gradient Class Activation Map. This map can highlight features and regions in an image that the deep learning model finds important, and manually inspect whether the model is performing in the desired way.
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 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
  • VGG model architecture
  • ResNet model architecture

you will learn

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

  • Using custom image data generators in Keras
  • Making predictions using deep learning models using Keras
  • Implementing Grad-CAM visualization
  • Implementing deep learning model performance metrics
  • Creating a radar plot to display the performance of multiple models

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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