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.
This liveProject is for intermediate Python programmers. To begin this liveProject, you will need to be familiar with:
- Intermediate Python 3.x and Jupyter notebooks
- Basics of Keras and OpenCV
- 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