Early and accurate detection of brain tumors is
very important to save the patient's life. Brain tumors are
generally diagnosed manually by a radiologist by analyzing the
patient’s brain MRI scans which is a time-consuming process.
This led to our study of this research area for finding out a
solution to automate the diagnosis to increase its speed and
accuracy. In this study, we investigate the use of Residual
Network deep learning architecture to diagnose and segment
brain tumors. We proposed a two-step method involving a
tumor detection stage, using ResNet50 architecture, and a
tumor area segmentation stage using ResU-Net architecture. We
adopt transfer learning on pre-trained models to help get the
best performance out of the approach, as well as data
augmentation to lessen the effect of data population imbalance
and hyperparameter optimization to get the best set of training
parameter values. Using a publicly available dataset as a testbed
we show that our approach achieves 84.3% performance
outperforming the state-of-the-art using U-Net by 2% using the
Dice Coefficient metric
