Abstract:
The Computed Tomography (CT) scan pictures are one of the most helpful
tools for diagnosing unusual regions in the liver. These picutres can be used to
identify abnormal areas of the liver and width of these areas in numerous
clinical applications. Manual and customary clinical testing needs a lot of
experienced pathologists and tedious interaction. Computer helps in
distinguishing pieces of proof which will further create and develop clinical
testing proficiency by identifying low accuracy and deficient determination
calculations. This paper proposes a Recurrent Residual U-Net (R2U-Net)
algorithm to classify the segmented liver. The experiments were led in CT liver
pictures that are available in clinic's picture chronicling and kaggle datasets.
Experiments have been carried out with a dataset of 400 liver CT pictures.
This dataset contains 200 liver CT pictures of patients with healthy liver and
200 liver CT pictures showing unhealthy liver. Finally, the proposed method
accomplished average accuracy, precision, sensitivity, specificity, and F1-
score values of 85.83%, 86.46%, 81.66%, 88.67%, and 95.23%, respectively.
