  Cancer is a disease that occurs as a result of the uncontrolled division and
proliferation of cells. Colon cancer is one of the most common types of cancer
in the world. Polyps that can be seen in the large intestine can cause cancer
if not removed with early intervention. Deep learning and image segmentation
techniques are used to minimize the number of polyps that goes unnoticed by the
experts during these interventions. Although these techniques perform well in
terms of accuracy, they require too many parameters. We propose a new model to
address this problem. Our proposed model requires fewer parameters as well as
outperforms the state-of-the-art models. We use EfficientNetB0 for the encoder
part, as it performs well in various tasks while requiring fewer parameters. We
use partial decoder, which is used to reduce the number of parameters while
achieving high accuracy in segmentation. Since polyps have variable appearances
and sizes, we use an asymmetric convolution block instead of a classic
convolution block. Then, we weight each feature map using a squeeze and
excitation block to improve our segmentation results. We used different splits
of Kvasir and CVC-ClinicDB datasets for training, validation, and testing,
while we use CVC- ColonDB, ETIS, and Endoscene datasets for testing. Our model
outperforms state-of-art models with a Dice metric of %71.8 on the ColonDB test
dataset, %89.3 on the EndoScene test dataset, and %74.8 on the ETIS test
dataset while requiring fewer parameters. Our model requires 2.626.337
parameters in total while the closest model in the state-of-the-art is U-Net++
with 9.042.177 parameters.
