Automatic Polyp Segmentation using U-Net-ResNet50

TL;DR

This paper presents an automatic polyp segmentation method using U-Net with ResNet50, achieving high accuracy on a challenge dataset to improve colorectal polyp detection during colonoscopy.

Contribution

The study introduces a U-Net-ResNet50 based model trained on Kvasir-SEG for improved polyp segmentation, demonstrating strong generalization and accuracy.

Findings

Achieved a dice coefficient of 0.8154

Attained a Jaccard index of 0.7396

High recall and precision around 0.85

Abstract

Polyps are the predecessors to colorectal cancer which is considered as one of the leading causes of cancer-related deaths worldwide. Colonoscopy is the standard procedure for the identification, localization, and removal of colorectal polyps. Due to variability in shape, size, and surrounding tissue similarity, colorectal polyps are often missed by the clinicians during colonoscopy. With the use of an automatic, accurate, and fast polyp segmentation method during the colonoscopy, many colorectal polyps can be easily detected and removed. The ``Medico automatic polyp segmentation challenge'' provides an opportunity to study polyp segmentation and build an efficient and accurate segmentation algorithm. We use the U-Net with pre-trained ResNet50 as the encoder for the polyp segmentation. The model is trained on Kvasir-SEG dataset provided for the challenge and tested on the organizer's dataset and achieves a dice coefficient of 0.8154, Jaccard of 0.7396, recall of 0.8533, precision of 0.8532, accuracy of 0.9506, and F2 score of 0.8272, demonstrating the generalization ability of our model.