COVID-19 X-Ray Images

 

COVID-19 x-ray image classification Age Group : (16+ years old)


 

COVID-19 x-ray images 

You will learn in this project how to solve an image classification problem in the healthcare sector. 

This project requires no prior AI knowledge. We’re going to design a machine learning (ML) model that classifies a set of x-ray images to diagnose COVID-19 patients. We will use COVID-Net, which is a Deep Convolutional Neural network (CNN) design that is tailored for the detection of COVID-19 cases from chest x-ray images. 

 

 

The problem

Classification problems have always been around. Humans have in all times tried to classify the world around them. We try to make sense of the world by making predictive models. We could build a model for x-ray images that can distinguish from among many lung X-ray images, the ones that are infected with Covid-19 virus and the ones that are healthy lungs. This model needs to train on a data set from x-ray images of diseased lungs and those of healthy people to learn to distinguish between people with COVID-19 and healthy people.

what is image classification?

Classification is a supervised learning technique in which the computer uses input data (in this project, x-ray images are the input data) to learn and then classify new observations. Further information can be found in the second course of Level2, Supervised Learning, in the AI Master Program. 

 

 

Learning outcomes

After completing this project, you will understand how to: 

  • Collect data and split it into training and testing datasets. 
  • Solve a classification problem by creating a ML model to classify a set of x-ray images of infected and healthy patients. 
  • Apply ML in real life scenario by deploying your COVID-19 AI model using our API into your own app or website so that you can start using your application straight away.

 


 

The data

You will use a dataset of x-ray images for patients that are positive or suspected to be infected with COVID-19. This dataset will be used to train your ML model on how to do the classification task on its own when it sees new x-ray images. You will need to make sure that your split your data into training dataset and testing dataset so that you know how to assess the performance of your ML model. 

 

 

projects steps:

1. meet your ai identity

Meet your AI Identity. You will teach your AI how to complete tasks as a human would. In due time, your AI Identity will grow more intelligent as you train it some more to take on different tasks with multiple projects. Eventually -in the future- you will be able to transfer your AI identity's intellect into a physical robot you can interact with in the real world.  


 

2. define the probelm. start your project

First, create a project and name it so you know what kind of project it is. Naming is important. A project combines all of the steps in solving a problem, from the pre-processing of datasets to model building, evaluation, and deployment. Using projects makes it easy to collaborate with others. After adding the project’s name and description, click on CREATE

 

 

3. select your ml model

In this step, you will need to select one of the ML models in order to solve your problem, that is classifying x-ray images. You will find a computer vision model (Make Me See), Natural Language Processing model (Make Me Read), Voice Recognition (Make Me Hear), and a tabular model (Make Me Count). In our case, you will need to train your AI transformer to how to see and classify x-ray images, therefore, you need to select MAKE ME SEE. 

More AI capabilities (models) will be added to the platform, so stay tuned! These models will help you to accelerate the process of creating a trained AI model that solves your problem, and thus export into your app to start using it!

 

 

4. collect your data

This is the most important step in any ML project. In fact, data preprocessing is 70-80% of any ML project. In this section, you will need to manually collect your x-ray images from reliable resources, such as Kaggle, or import the covid-19 dataset from our data library. You need to classify the data into “Infected” data class and “Healthy” data class. Then upload x-ray images. You can use a COVID-19 dataset from the dataset library.

 

 

5. train your model 

In this step, you will start training your AI transformer (i.e. ML model) on your collected data. It is simpley where you show your AI how to classify an x-ray image of an infected person from an x-ray image of a healthy person, so that when the AI sees a new x-ray images, it can recognize whether it belongs to a healthy or covid-19 infected patient. Click TRAIN ME to start the training. This may take a few moments.


 

6. test your model

Right now, and after you completed training your model, you are ready to test it using a new sample x-ray image. You can test your model using one of the x-ray images from the testing dataset (see point 4). Just make sure that you dont use an image from the dataset that you used in the training part, otherwise, you are cheating! Upload or import the sample image to assess the performance in the preview section. If you like the confedence score, you can export your model. Otherwise, go back to the "collect data" step and check whether you have used suffient number of images in the training dataset. 


 

export your model

If you are happy with the testing results, you can "Export" your ML model using our API into your own website, application or even to your robot or any machine! If you are not happy with the confidence score, you will need to check your training data classes again, and make sure that you have clean and sufficient number of examples for each data class. Keep in mind the concept of GIGO (garbage in, garbage out) which means that the quality of your ML model output is determined by the quality of your input, i.e. training dataset.