  Colorectal cancer from the appearance of polyps that can be benign or
malignant is one of the most fatal diseases in the world. To find these polyps
in patients, colonoscopy is performed, which is a very efficient technique in
this case. Clinically, detecting and segmenting these polyps in order to
determine their presence or not is a very difficult process that requires a lot
of time and experience from professionals, depending directly on these factors.
Therefore, it becomes increasingly important to have an automatic, effective
and reliable method of detecting and segmenting these polyps, making diagnoses
faster and more accurate. In order to assist in the development of a method, we
proposed the U-Net-MobileNetV2 model, which is the combination of two neural
networks, where one acts as an encoder for the other and is responsible for
learning image resources. Our experiments generated satisfactory results,
demonstrating a good performance and good segmentation. U-Net-MobileNetV2
achieved a Dice Coefficient of 89.71% and an IoU of 81.64% for the Kvasir-SEG
dataset, where both are higher than the results obtained by other
state-of-the-art models.
