  The segmentation of diseases is a popular topic explored by researchers in
the field of machine learning. Brain tumors are extremely dangerous and require
the utmost precision to segment for a successful surgery. Patients with tumors
usually take 4 MRI scans, T1, T1gd, T2, and FLAIR, which are then sent to
radiologists to segment and analyze for possible future surgery. To create a
second segmentation, it would be beneficial to both radiologists and patients
in being more confident in their conclusions. We propose using a method
performed by radiologists called image segmentation and applying it to machine
learning models to prove a better segmentation. Using Mask R-CNN, its ResNet
backbone being pre-trained on the RSNA pneumonia detection challenge dataset,
we can train a model on the Brats2020 Brain Tumor dataset. Center for
Biomedical Image Computing & Analytics provides MRI data on patients with and
without brain tumors and the corresponding segmentations. We can see how well
the method of image subtraction works by comparing it to models without image
subtraction through DICE coefficient (F1 score), recall, and precision on the
untouched test set. Our model performed with a DICE coefficient of 0.75 in
comparison to 0.69 without image subtraction. To further emphasize the
usefulness of image subtraction, we compare our final model to current
state-of-the-art models to segment tumors from MRI scans.
