Medical Image Classification using Tensorflow

Offered By
Coursera Project Network
In this Guided Project, you will:

Import and compile a Residual Convolutional Network (Resnet).

Train a Resnet to identify pleural effusion in chest x-ray (CXR) images.

Use the fully trained Resnet for inference functions identifying effusion.

Clock2 hours
AdvancedAdvanced
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

The medical imaging industry is set to see 9 and a half billion dollars in growth in just a few years, mostly due to advances in AI imaging technologies. AI integration with medical imaging is expected to gain traction as it enables increased productivity, improved accuracy, and reduced errors in the diagnosis performed by technicians and radiologists. The use of AI will also automate the labor-intensive manual segmentation and enable technicians to identify abnormalities, in turn, accelerating the treatment process. Furthermore, AI platforms are also being developed for hospitals and health systems to help clinicians in making quick decisions and improving patient outcomes. Ultimately, this field of research will benefit from more minds refining the technology. This project will get you started in using Python and Tensorflow/Keras for advanced medical imaging. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

tensorflow in productionimage classificationhealth informatics analysis

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Preprocess medical imaging data

  2. Compile a neural network model -Part 1

  3. Compile a neural network model -Part 2

  4. Build and Train a Resnet Model to recognize lung effusion

  5. Making Predictions in Inference

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

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