Four-Project Series
Transfer Learning for Dicom Image Classification you own this product
- prerequisites
- intermediate Python • basics of deep learning • basics of Keras and OpenCV
- skills learned
- building convolutional deep learning architectures with basic functional components • deploying deep learning models using Keras • tuning models to improve performance
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team
5, 10 or 20 seats+ for your team - learn more
In this series of liveProjects, you’ll use deep learning to build an image classification model that can perform early diagnostics of COVID-19. Your model will examine X-rays and CT scans and seek to classify them as either COVID or non-COVID types. You’ll start by training custom deep learning models from scratch, and then experiment with transfer learning using premade models. Familiarity with both these approaches will ensure you’re able to design a powerful solution for any deep learning problem, model, or dataset. Finally, you’ll test and report on the efficiency of trained models to decide which model delivers the best results.
These projects are designed for learning purposes and are not complete, production-ready applications or solutions.
here's what's included
Project 1 Build a VGG16 Model
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 2 Build a ResNet Model
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.
Project 3 Transfer Learning
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.
Project 4 Evaluate and Explain DL Models
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.
book resources
When you start each of the projects in this series, you'll get full access to the following book for 90 days.
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Transfer Learning for Dicom Image Classification project for free
Transfer Learning for Dicom Image Classification project for free
team
monthly
annual
$49.99
$499.99
only $41.67 per month
- five seats for your team
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
- choose another free product every time you renew
- choose twelve free products per year
- exclusive 50% discount on all purchases
-
![]()
Transfer Learning for Dicom Image Classification project for free
Prerequisites
This liveProject is for intermediate Python programmers. To begin this liveProject, you will need to be familiar with the following:
TOOLS
- Intermediate Python 3.x and Jupyter notebooks
- Basics of Keras and OpenCV
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 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.
