Transfer Learning for Image Classification

Build a ResNet Model

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

filed under

Data Science

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liveProject

$17.99

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. $17.99 $29.99 you save: $12 (40%) self-paced learning

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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 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 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.