Abstract
One of the most prominent machine learning advantages in the medical
industry is the early detection of disease. Automatic kidney detection
is of great importance for rapid diagnosis and treatment, where related
diseases occupy over 73,750 new cases in the US in 2020 [1]. Today,
the performance of diagnosis has been by highly trained radiologists.
However, the complex structures contribute to speckle noise and
inhomogeneous intensity profiles. Thus, there is a necessity to automate
segmentation on kidney ultrasounds using U-Net Deep Learning
architectures - an innovative solution for Medical Imaging Analysis. In
this research, our focus is on the comparison of Attention U-Net in the
context of different backbones such as VGG19, ResNet152V2, and
EfficientNetB7. By providing this comparison, we will accomplish a
survey for future researchers to more effectively decide on which
Attention U-Net architecture to utilize for their segmentation projects.