  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.
