# Polyp Detection and Segmentation using Mask R-CNN: Does a Deeper Feature Extractor CNN Always Perform Better?

## TL;DR

This study evaluates the effectiveness of different CNN feature extractors within Mask R-CNN for polyp detection and segmentation, examining whether deeper networks or better datasets yield superior results, and proposes an ensemble method to enhance performance.

## Contribution

It systematically compares various CNN backbones in Mask R-CNN for polyp detection, analyzes the impact of dataset size, and introduces an ensemble approach for improved accuracy.

## Key findings

- Best performance with 72.59% recall and 80% precision
- Achieved state-of-the-art segmentation metrics
- Ensemble method further improved detection results

## Abstract

Automatic polyp detection and segmentation are highly desirable for colon screening due to polyp miss rate by physicians during colonoscopy, which is about 25%. However, this computerization is still an unsolved problem due to various polyp-like structures in the colon and high interclass polyp variations in terms of size, color, shape, and texture. In this paper, we adapt Mask R-CNN and evaluate its performance with different modern convolutional neural networks (CNN) as its feature extractor for polyp detection and segmentation. We investigate the performance improvement of each feature extractor by adding extra polyp images to the training dataset to answer whether we need deeper and more complex CNNs or better dataset for training in automatic polyp detection and segmentation. Finally, we propose an ensemble method for further performance improvement. We evaluate the performance on the 2015 MICCAI polyp detection dataset. The best results achieved are 72.59% recall, 80% precision, 70.42% dice, and 61.24% Jaccard. The model achieved state-of-the-art segmentation performance.