Abstract:
Brain tumor is the spread of abnormal cells in the brain. Out of several kinds of brain
tumor gliomas is the most dangerous with low survival rate and difficult to detect manually due to irregular form and confusing boundaries. Magnetic Resonance Imaging
is the most widely used imaging modality that allows radiologist to look inside brain
by utilizing radio waves and magnet but the manual identification of tumor region is
tedious task. Therefore, a reliable and automatic segmentation and prediction is necessary for segmentation of brain tumor and prediction. However due to complexity
and unavailability of resources to train deep learning algorithms, it is complex to identify the tumorous and non-tumorous region. So, in this paper, a reliable and efficient
variant of UNET i.e., VGG19-UNET for segmentation of brain tumor and ensemble
learning model for survival prediction is proposed. Specifically, an encoder part of the
UNET is a pretrained VGG19 followed by the adjacent decoder part. Meanwhile, the
ensemble voting classifier of Naïve Bayes and Random Forest was trained for survival
prediction. The datasets we are using for segmentation is BRATS’20 which comprises
of four different MRI modalities and one target mask file. Whereas, the datasets of
survival prediction is also BTARS’20 which is comma separated file containing different
features. Above mentioned algorithm resulted in dice coefficient score of 0.81, 0.86 and
0.88 for enhancing, core and whole tumor whereas the accuracy of overall survival is
62.7%.
