Jun 29, 2021
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Service categories
Service Lines
Artificial Intelligence
Web Development
Domain focus
Healthcare
Programming language
HTML
Java
Python
Challenge
The company has been developing a comprehensive healthcare platform. Treating pneumonia has been one of its focus areas due to COVID, and it wanted to build a pneumonia diagnosis tool for the platform.
The tool was destined to analyze lung X-ray images and identify signs of pneumonia using machine learning (ML), an artificial intelligence (AI) technique. The company didn’t have relevant in-house experts, so they reached out for help and found it with Elinext.
The company has been developing a comprehensive healthcare platform. Treating pneumonia has been one of its focus areas due to COVID, and it wanted to build a pneumonia diagnosis tool for the platform.
The tool was destined to analyze lung X-ray images and identify signs of pneumonia using machine learning (ML), an artificial intelligence (AI) technique. The company didn’t have relevant in-house experts, so they reached out for help and found it with Elinext.
Solution
We chose InceptionV3 developed by Google Research Lab. To create a static HTML5 web page, we deployed a web server in a Docker container. On that page, a user can upload a lung image and get feedback. The image is sent for processing through the HTTP protocol.
We needed to train complex models with huge datasets fast. To do that, we rented an Amazon Web Services (AWS) g3s.xlarge instance and used Deep Learning Base AMI (Ubuntu 18.10). The latter is a powerful machine boasting 16GB of RAM, a 4-core CPU and an Nvidia Tesla M60 GPU. It was a perfect fit for the task.
We chose InceptionV3 developed by Google Research Lab. To create a static HTML5 web page, we deployed a web server in a Docker container. On that page, a user can upload a lung image and get feedback. The image is sent for processing through the HTTP protocol.
We needed to train complex models with huge datasets fast. To do that, we rented an Amazon Web Services (AWS) g3s.xlarge instance and used Deep Learning Base AMI (Ubuntu 18.10). The latter is a powerful machine boasting 16GB of RAM, a 4-core CPU and an Nvidia Tesla M60 GPU. It was a perfect fit for the task.
Results
The tool we’ve built can help reduce human error in identifying pneumonia. This is particularly useful during the pandemic when doctors are overloaded and might overlook some signs of illness.We can also scale the model up to identify some other diseases. Scaling the model down will help integrate it into other systems, speed things up and allow for the analysis of multiple images simultaneously.
The tool we’ve built can help reduce human error in identifying pneumonia. This is particularly useful during the pandemic when doctors are overloaded and might overlook some signs of illness.We can also scale the model up to identify some other diseases. Scaling the model down will help integrate it into other systems, speed things up and allow for the analysis of multiple images simultaneously.
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