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Colorectal cancer from the appearance of polyps that can be benign ormalignant is one of the most fatal diseases in the world. To find these polypsin patients, colonoscopy is performed, which is a very efficient technique inthis case. Clinically, detecting and segmenting these polyps in order todetermine their presence or not is a very difficult process that requires a lotof time and experience from professionals, depending directly on these factors.Therefore, it becomes increasingly important to have an automatic, effectiveand reliable method of detecting and segmenting these polyps, making diagnosesfaster and more accurate. In order to assist in the development of a method, weproposed the U-Net-MobileNetV2 model, which is the combination of two neuralnetworks, where one acts as an encoder for the other and is responsible forlearning image resources. Our experiments generated satisfactory results,demonstrating a good performance and good segmentation. U-Net-MobileNetV2achieved a Dice Coefficient of 89.71% and an IoU of 81.64% for the Kvasir-SEGdataset, where both are higher than the results obtained by otherstate-of-the-art models.

Polyp Segmentation in Colonoscopy Images using U-Net-MobileNetV2