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braintumordetection's Introduction

Brain Tumor detection using MRI Images

Team Members:

  1. Sachin Karve
  2. Manovikas Ramaswamy
  3. Ritwik Jadhav
  4. Aswin Prasad

Abstract

Considering the medicare expenses in the developed country like US and around the world, it is no more a feasible option to casually consult doctor for first level of uneasiness. Also, in the developing countries like South Africa, Nigeria, Kenya people suffer immensely when it comes to medicare and quick attention required for certain cases. Initial negligence to scenarios like this create immense complications and might lead to major health issues down the line. There has been a lot of talk about brain tumor and the consequences of its late detection.

According to latest reports it affects around 24000 adults and 3500 children per year in the US alone. One major reason why we are losing people to brain tumor is late detection of the disease. Reports state that an early detection of tumors can save up to 13% . It is unfair to not leverage the technology of our times to develop a solution for this and hence we propose the solution to detect brain tumor in early stage using MRI scans through Artificial intelligence and computer vision.

On the other hand, in USA, Insurance companies play a vital role in the health related payment procedures. Insurance companies check the health reports of patients while registering to their company or while reimbursing the amount to the patients. Insurance companies consult the hospitals or clinics where the procedure has been carried out for the authenticity or verification of the corresponding procedure. In such cases, an application which directly gives a result to the insurance companies which verifies whether the patients has the tumor or not is highly advantageous. Insurance companies or companies can freely use such service to get an idea of the situation before or after consulting the doctor.

Taking such scenarios into account, we plan to develop an application which employes ML model and uses a brain tumor image dataset to verify the prescence of the tumor. It will be used to identify the tumorous MRI scans in real time and give out the results on our web application. This project can be extended to detect the presence of Lung cancer using CT scan images of lungs.

Design thinking

Persona/Target users

  1. Insurance companies - With the health expenditure in the US increasing YoY and the never before reliance on insurance companies in health industry. It is time also to start verifying the medical conditions and do some sanity checks to verify insurance claims.

  2. Patients

  • Insured patients can upload this MRI as an attachment to medical record submission for insurance reimbursement, this can reduce hassle by reducing the documenation needed to file a claim.
  • Patients in developing countries(Where there are scarcity of good doctors) can use this facility to identify tumors in early stage and can deicde on further treatments required.

Hill statement

Improving the access to early tumor detection mechanism to people for whom finding specialised doctors are difficult. Simplyfy the insurance claim work-flow and thus reducing hassle in both patient and insurance companies side.

Architecture Diagram

System Architecture Diagram

Technology stack

  1. IBM Watson
  2. React.js
  3. Node.js
  4. IBM Bluemix
  5. AWS EC2

braintumordetection's People

Contributors

manovikasr avatar ritwikjadhav avatar aswinprasadsjsu avatar

Stargazers

Abdulrhman elwakeel avatar

Watchers

James Cloos avatar

braintumordetection's Issues

How to obtain and handle user's MRI and CT scan?

How can the user obtain the MRI or any imaging and upload to the this web application? Since there is no database shown in architecture diagram, how do you store the uploaded MRI and CT scan?

Deployment of the project.

The tech stack mentions React.js, but I don't see where the Javascript code would be deployed in the architecture diagram. I think it would be good to add to the diagram where the front end code will reside and how it will interact with your machine learning model.

The over fitting problem

Because you use CT scan as the graph which can make some errors when doing the learning process. The model may also over fit and cause some errors as well.

How you let your customer use this technology?

I am a little confused about how this project executes. The project wants to detect early brain tumors using MRI in developed countries. How do you make people in these countries to do MRI?

Even the AI can detect brain tumors using MRI images. I am not sure how you make your customers, who are the people in developed countries, do an MRI and scan their body.

How to demonstrate potential business value.

There is obvious business value here, but it would be good to describe where you would first try to market this and what kind of customer you're targeting. Seems like insurance companies would be a good place to start since early detection could save them a lot of money on treatment down the line. Most insurance companies cover 1 preventative care visit per year. I think they would probably pay to have an algorithm to screen CT scan images taken during these preventative care visits.

Privacy Problem

The datasets for building the Machine Learning Model may be raising the privacy issue and may not be able to get enough data.

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