# Beyond Human Vision: Revolutionizing the Localization of Diminutive Sessile Polyps in Colonoscopy

## TL;DR

This paper introduces an AI system that improves the detection of small, flat polyps during colonoscopies, helping reduce human error in colorectal cancer prevention.

## Contribution

A novel AI-based assistant using YOLO-V8 for localizing diminutive sessile polyps in colonoscopy images with high precision and recall.

## Key findings

- The AI system achieved 96.4% precision and 93.89% recall in detecting diminutive sessile polyps.
- The dataset combining white light and narrow-band imaging improved model performance.
- Polyp size and coordinate analysis validated the dataset's suitability for training.

## Abstract

Gastrointestinal disorders, such as colorectal cancer (CRC), pose a substantial health burden worldwide, showing increased incidence rates across different age groups. Detecting and removing polyps promptly, recognized as CRC precursors, are crucial for prevention. While traditional colonoscopy works well, it is vulnerable to specialist errors. This study suggests an AI-based diminutive sessile polyp localization assistant utilizing the YOLO-V8 family. Comprehensive evaluations were conducted using a diverse dataset that was assembled from various available datasets to support our investigation. The final dataset contains images obtained using two imaging methods: white light endoscopy (WLE) and narrow-band imaging (NBI). The research demonstrated remarkable results, boasting a precision of 96.4%, recall of 93.89%, and F1-score of 94.46%. This success can be credited to a meticulously balanced combination of hyperparameters and the specific attributes of the comprehensive dataset designed for the colorectal polyp localization in colonoscopy images. Also, it was proved that the dataset selection was acceptable by analyzing the polyp sizes and their coordinates using a special matrix. This study brings forth significant insights for augmenting the detection of diminutive sessile colorectal polyps, thereby advancing technology-driven colorectal cancer diagnosis in offline scenarios. This is particularly beneficial for gastroenterologists analyzing endoscopy capsule images to detect gastrointestinal polyps.