A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor
  Segmentation
Tejashwini P S Thriveni J Venugopal K R
Brain tumor is deliberated as one of the severe health complications which
lead to decrease in life expectancy of the individuals and is also considered
as a prominent cause of mortality worldwide. Therefore, timely detection and
prediction of brain tumors can be helpful to prevent death rates due to brain
tumors. Biomedical image analysis is a widely known solution to diagnose brain
tumor. Although MRI is the current standard method for imaging tumors, its
clinical usefulness is constrained by the requirement of manual segmentation
which is time-consuming. Deep learning-based approaches have emerged as a
promising solution to develop automated biomedical image exploration tools and
the UNet architecture is commonly used for segmentation. However, the
traditional UNet has limitations in terms of complexity, training, accuracy,
and contextual information processing. As a result, the modified UNet
architecture, which incorporates residual dense blocks, layered attention, and
channel attention modules, in addition to stacked convolution, can effectively
capture both coarse and fine feature information. The proposed SLCA UNet
approach achieves good performance on the freely accessible Brain Tumor
Segmentation (BraTS) dataset, with an average performance of 0.845, 0.845,
0.999, and 8.1 in terms of Dice, Sensitivity, Specificity, and Hausdorff95 for
BraTS 2020 dataset, respectively.