Project
MLOps-enabled Polyp Detection System
Year
2025
Service
Computer Vision
Polyp Detection System
Project Overview
This project is a web-based, AI-powered polyp detection and segmentation system designed to assist medical professionals during colonoscopy procedures. It uses a YOLOv8 segmentation model to analyze live camera feeds, medical images, and videos, providing real-time visual detection and patient-wise diagnosis tracking through an intuitive dashboard.
Project Overview
This project is a web-based, AI-powered polyp detection and segmentation system designed to assist medical professionals during colonoscopy procedures. It uses a YOLOv8 segmentation model to analyze live camera feeds, medical images, and videos, providing real-time visual detection and patient-wise diagnosis tracking through an intuitive dashboard.
The Problem
Manual identification of polyps during colonoscopy is time-sensitive and highly dependent on the clinician’s experience, which can lead to missed or misclassified abnormalities. Traditional systems lack real-time AI assistance and structured tracking of patient diagnosis history, making it difficult to ensure consistency, accuracy, and efficient follow-up.
The Problem
Manual identification of polyps during colonoscopy is time-sensitive and highly dependent on the clinician’s experience, which can lead to missed or misclassified abnormalities. Traditional systems lack real-time AI assistance and structured tracking of patient diagnosis history, making it difficult to ensure consistency, accuracy, and efficient follow-up.
The Solution
The system integrates a YOLOv8-based segmentation model into a web platform that enables real-time polyp detection from live video streams and uploaded medical files. It provides patient management, automated result storage, and visual overlays to support faster and more reliable clinical decision-making during examinations.
The Solution
The system integrates a YOLOv8-based segmentation model into a web platform that enables real-time polyp detection from live video streams and uploaded medical files. It provides patient management, automated result storage, and visual overlays to support faster and more reliable clinical decision-making during examinations.
The Result
The platform improves detection accuracy and consistency, reduces the risk of missed polyps, and enhances workflow efficiency for medical professionals. It delivers real-time visual assistance, organized patient diagnosis records, and a more reliable, data-driven approach to clinical screening and follow-up.
The Result
The platform improves detection accuracy and consistency, reduces the risk of missed polyps, and enhances workflow efficiency for medical professionals. It delivers real-time visual assistance, organized patient diagnosis records, and a more reliable, data-driven approach to clinical screening and follow-up.
