IEEE 2022 - AI Brain Tumor Detection
sumMRI
During my first year as part of Northwestern’s IEEE Student Chapter I helped create a web app that diagnoses brain tumors from MRI scans with a classification ML model.
The Problem
- Brain cancer is the 10th leading cause of death, over 250,000 die from brain tumors every year
- Despite decades of research in the field, medical diagnostic errors have been at 5%, and 40 million errors in diagnostic imaging are made annually
- Radiologist burnout hovers around 35%, and the average number of images requiring interpretation per minute has increased sevenfold from 1999 to 2009. Despite increases in radiologist staffing, radiologists in 2015 had to interpret images every three to four seconds; all of that can be attributed to poor diagnoses.
Machine assisted medical imaging hsa a large potential to save lives, as early diagnosis of tumors can improve cancer patient rates by up to 400%. Existing solutions are underutilized in hospitals due to factors including inaccessibility and cost.
Our Solution
sumMRI is a web platform that makes it simpler and easier for anyone to diagnose brain tumors from MRI scans with a classification ML model. Using two convolutional neural networks (CNNs), sumMRI can detect if a brain tumor is present in an MRI scan and where the specific brain tumor is located. This tool can help neurologists short-circuit human error while examining MRI scans.
AI Demo + Technology:
This is the part I created! As part of the team, I focused on building the ML Image Segmentation Model, which detects the location of a brain tumor.
Project Slides:
Website Demo:
Here’s a demo that other team members worked on of the program running on a website.
