Assistive Diagnostic Tool for Brain Tumor Detection using Computer Vision

TL;DR

This paper presents an assistive diagnostic tool utilizing Mask R CNN and transfer learning to detect and segment brain tumors from MRI images, achieving 90% accuracy and aiding medical professionals.

Contribution

It introduces a computer vision-based system with transfer learning for brain tumor detection, providing a practical tool for clinical use with high accuracy.

Findings

90% segmentation accuracy compared to ground truth

Effective use of transfer learning with Mask R CNN

Flask application for real-time diagnosis

Abstract

Today, over 700,000 people are living with brain tumors in the United States. Brain tumors can spread very quickly to other parts of the brain and the spinal cord unless necessary preventive action is taken. Thus, the survival rate for this disease is less than 40% for both men and women. A conclusive and early diagnosis of a brain tumor could be the difference between life and death for some. However, brain tumor detection and segmentation are tedious and time-consuming processes as it can only be done by radiologists and clinical experts. The use of computer vision techniques, such as Mask R Convolutional Neural Network (Mask R CNN), to detect and segment brain tumors can mitigate the possibility of human error while increasing prediction accuracy rates. The goal of this project is to create an assistive diagnostics tool for brain tumor detection and segmentation. Transfer learning was used with the Mask R CNN, and necessary parameters were accordingly altered, as a starting point. The model was trained with 20 epochs and later tested. The prediction segmentation matched 90% with the ground truth. This suggests that the model was able to perform at a high level. Once the model was finalized, the application running on Flask was created. The application will serve as a tool for medical professionals. It allows doctors to upload patient brain tumor MRI images in order to receive immediate results on the diagnosis and segmentation for each patient.