X Ray Based Pneumonia Detection

In the project ?Pneumonia Detection Through X-Ray?, a web application will be developed for providing a user-friendly GUI at frontend while pneumonia will be detected by a trained deep learning model embedded on the server at backend. Deep learning is a powerful tool that can be used to locate pneum

Project Title

X Ray Based Pneumonia Detection

Project Area of Specialization

Artificial Intelligence

Project Summary

In the project “Pneumonia Detection Through X-Ray”, a web application will be developed for providing a user-friendly GUI at frontend while pneumonia will be detected by a trained deep learning model embedded on the server at backend. Deep learning is a powerful tool that can be used to locate pneumonia in patients through chest x-ray images. The unique features associated with deep learning techniques are to perform classification tasks directly through images and achieve high level accuracy. Deep learning models require substantial computing power to train along with a large amount of labelled data, which in our case will be images of diseased and healthy chest x-rays. Convolutional neural networks are used for dealing with images because of their ability to learn features directly from images leading to automated features extraction instead of manually extracting features in order to classify images. The goal of this project is to build a deep learning model that could automatically locate pneumonia-causing features from the chest x-ray images instead of requiring a doctor/radiologist to do it manually, thus saving time, money and reducing risk. With the help of this application, X-Ray technicians or radiologists will be able to give a precise opinion to the patient about their lungs and how much they are affected due to pneumonia and can understand the condition of patient more accurately either he is in a serious condition or not. X-ray images of healthy and pneumonia affected lungs will be fed to our deep learning model, which will be trained based on those images. Once the model is trained, then it will be deployed on the server of our web application. The web application is necessary for this because doctors/technicians cannot deal directly with the model efficiently. They need a user-friendly interface. Patient’s chest X-ray will be given as an input image on the web application. Assuming that the given input is X-Ray of a pneumonia patient, our model will be able to predict either it is healthy or diseased. If it diseased, then our model will annotate the diseased portion of the x-ray image. After the prediction of the model the result will be shown on our web application. Through this way patients can get timely access to treatment and save time. Doctors/technicians can save a lot of their time and reduce their workload as well.  

Project Objectives

Goal of this project is to develop and launch a web application for the

technicians/radiologists to guide the patient about its chest condition and to clarify

that how much it is affected from pneumonia.

Following are the objectives of this project:

• Train a deep learning model that has a capability to detect Pneumonia through     X-Ray precisely.
• Develop a Web Application linked with the server on which our model would be deployed.
• Annotate the diseased part of the X-Ray if any.
• Help radiologists/technicians.
• Provided accurate and fast diagnosis for Pneumonia patients.
• Help remote areas where doctors are not available. Analyzing and reading an x-ray image requires domain expertise and experience. This application can read the images and tell us the results.
• Save time for both patients and doctors.
• Automation of disease diagnosis.

Project Implementation Method

Following is the project implementation methodology:

• Chest X-Ray data collection
• Data preproccessing
• Split data into three sets (training data, testing data, validation data)
• Train the deep learing model
• Performance evaluation of deep learning model
• Test the trained model
• Annotation of pneumonia infected parts
• Develop a web application using MEAN stack
• Deployment of deep learning model and web application on server
• Test the server
• Allocate hosting

Benefits of the Project

Pneumonia is the no. 1 killer of Pakistan’s children. Pakistan is ranked top 3rd country in world in children deaths (58,000) under five in 2018. According to a report the mean age is 63.6 ± 16.5 years and 52.16% are males. Our project will decrease the death rate of pneumonia infected people by providing timely diagnosis.

It’s a time taking process for the patient to wait for the radiologist/expertise. Our project will save time by providing early diagnosis.

Newbies comes to hospitals every year and join this occupation. They have lack of experience to analyze the x-ray. They need to consult their seniors in most cases. Instead of this they can get assistance from our web application.

Human error can occur due to overloading of work on doctors. Our project will reduce their workload and error.

In remote areas where there are a few expert doctors. Our project will help treating the disease without any delay.

Technical Details of Final Deliverable

We will build a deep learning model and a web application. We will deploy both these on server. Deep learning model will help users in pneumonia detection and annotation of pneumonia infected parts. Web application will help user to interact with the system in a more friendly way. User will be able to get pneumonia prediction in one click after uploading chest x-ray image. Our final product will be web application where deep learning model will be integrated.

Final Deliverable of the Project

Software System

Core Industry

Medical

Other Industries

Health

Core Technology

Artificial Intelligence(AI)

Other Technologies

Cloud Infrastructure, Big Data

Sustainable Development Goals

Good Health and Well-Being for People, Sustainable Cities and Communities, Life on Land

Required Resources

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