Squeeze Excitation Embedded Attention UNet for Brain Tumor Segmentation
Gaurav Prasanna John Rohit Ernest Lalitha G Sathiya Narayanan
Deep Learning based techniques have gained significance over the past few
years in the field of medicine. They are used in various applications such as
classifying medical images, segmentation and identification. The existing
architectures such as UNet, Attention UNet and Attention Residual UNet are
already currently existing methods for the same application of brain tumor
segmentation, but none of them address the issue of how to extract the features
in channel level. In this paper, we propose a new architecture called Squeeze
Excitation Embedded Attention UNet (SEEA-UNet), this architecture has both
Attention UNet and Squeeze Excitation Network for better results and
predictions, this is used mainly because to get information at both Spatial and
channel levels. The proposed model was compared with the existing architectures
based on the comparison it was found out that for lesser number of epochs
trained, the proposed model performed better. Binary focal loss and Jaccard
Coefficient were used to monitor the model's performance.