COVID-19 and Pneumonia Classification Using Deep Learning

The motive of our project is to create a diagnosis mobile application that takes the image of a X-ray as an input and gives output in the form of image, tagged as COVID positive, Viral Pneumonia or Normal. With the rise in COVID cases the increasing pressure on health sector around the world is alar

Project Title

COVID-19 and Pneumonia Classification Using Deep Learning

Project Area of Specialization

Artificial Intelligence

Project Summary

The motive of our project is to create a diagnosis mobile application that takes the image of a X-ray as an input and gives output in the form of image, tagged as COVID positive, Viral Pneumonia or Normal. With the rise in COVID cases the increasing pressure on health sector around the world is alarming be it developed countries or developing countries. Our work will provide a helping hand to healthcare staff and possibly resource will be used more efficiently.

Research comprises of end-to-end image segmentation and image classification models. Then we will move to development part.

In the development part, we will develop a mobile application that will let the user to perform diagnosis of an X-ray image that can be selected from local storage on the mobile. Final product will be a diagnosis application for COVID and Pneumonia.

Project Objectives

• A deep learning model capable of classifying COVID-19, Pneumonia and Normal Patient X-ray images.
• A real time model taking X-ray images and predict the result, more efficient and accurate than the existing one.
• A mobile application directly interacts with users by taking X-ray images and classify the provided image i.e., COVID-19, Normal or Normal.

Project Implementation Method

We’ve proposed an implementation that includes first the segmentation of the provided X-ray images using end-to-end models. Then another neural network is used to for image classification.

In the first step, the image is segmented using a pre-trained neural network known as UNET that is popular biomedical image processing to detection the presence of infection. In this way we’ll be able to extract left and right lungs removing unwanted features.

This data is fed into a machine learning algorithm which trains and generates a model that will classify images. The model is deployed inside a mobile application where it can give quick and accurate results.

Benefits of the Project

1. The mobile application can be used in mobiles for diagnosis purposes.
2. It can be used on normal mobile phones and doesn’t require special equipment for deployment.
3. Research results can be studied in the future as a basis for further work.
4. Compared to older implementations, this framework hopes to provide a more lightweight and accurate feature set that can detect COVID and Pneumonia with more accuracy and efficiency.

Technical Details of Final Deliverable

A Mobile Application where users can select an image of an X-ray from the local memory of the mobile as an input to the application and it will predict whether patient has COVID, Pneumonia or normal.

Final Deliverable of the Project

Software System

Core Industry

Medical

Other Industries

IT

Core Technology

Artificial Intelligence(AI)

Other Technologies

Artificial Intelligence(AI)

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com