Use Computer Vision for Image Recognition of Any Concepts with Clarifai
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Introduction
In this article, we will explore the process of building an X-ray classifier designed to identify COVID-19 symptoms using the Clarifai platform. This demonstration highlights Clarifai's versatility in handling various types of input data, from X-rays to infrared and multi-spectral bands. Here’s how to create an application that can effectively recognize and classify chest X-ray images as either normal or indicative of COVID-19.
Step 1: Setting Up the Application
To begin, we will create two concepts in the application: "COVID" for images depicting COVID-19 symptoms and "Normal" for images showing normal lung behavior. After defining these concepts, the next step is to upload data, specifically chest X-rays. This can be accomplished by efficiently browsing through files on your local file system and selecting multiple images for bulk uploading.
Step 2: Data Management
Once the X-ray images are uploaded, you can monitor the progress in the data mode. Switching to explorer mode allows you to view the uploaded data and manage it effectively. Importantly, it offers the ability to select multiple images and label them in bulk, which accelerates the labeling process necessary for training the model.
In this instance, we have labeled several X-ray examples exhibiting COVID-19 symptoms. Subsequently, we’ll upload additional examples that demonstrate normal chest conditions, which also undergo the same indexing and labeling processes.
Step 3: Model Creation
With both categories populated, we are ready to create a model. On the left-hand side, we initiate the model creation process, selecting a context-based classifier. This model benefits from being pre-trained on millions of diverse imagery examples, enabling it to learn the new categories—COVID and Normal—swiftly.
An important consideration during this setup is enabling “mutually exclusive concepts,” which dictate that an X-ray cannot simultaneously depict both normal and COVID-19 symptoms. This teaching approach strengthens the model's ability to differentiate between the two distinctly different categories.
Step 4: Model Training and Evaluation
After training the model, we're able to evaluate its performance using various metrics. The evaluation results provide insights, including precision, recall, confusion matrices, and the PR curve for each concept.
To test the model's effectiveness on new data, simply upload additional X-ray images to your app. The custom-trained model will then generate predictions, enabling us to explore its accuracy. For instance, if the model predicts that an image exhibits normal characteristics with 100% confidence, it confirms the effectiveness of our training process. Conversely, an X-ray that shows COVID-19 symptoms is also confirmed with the same confidence level.
This straightforward approach illustrates how the Clarifai platform simplifies the creation of classifiers, even within complex domains such as medical imaging.
Keywords
- Clarifai
- Image Recognition
- COVID-19
- X-ray Classifier
- Machine Learning
- Data Management
- Model Training
- AI
FAQ
Q1: What is Clarifai?
A1: Clarifai is an artificial intelligence platform that provides tools for image and video recognition, allowing users to build classifiers and manage data effectively.
Q2: How do I create concepts for classification in Clarifai?
A2: You can create concepts by defining specific categories for your data, such as "COVID" and "Normal" in this case, and then uploading images to label them accordingly.
Q3: What does mutually exclusive mean in the context of model training?
A3: Mutually exclusive concepts mean that the model is trained to differentiate between categories where the presence of one excludes the presence of the other, such as not being able to exhibit both COVID-19 symptoms and normal symptoms at the same time.
Q4: How can I evaluate the performance of my model?
A4: After training your model, you can use metrics like precision, recall, and confusion matrices to evaluate its performance and identify how well it classifies different concepts.
Q5: Can Clarifai handle different types of input data?
A5: Yes, Clarifai is capable of processing various types of input data, including X-rays, infrared images, and multi-spectral bands, making it versatile for different applications.