arxiv_cv 95% Match Research Paper Gastroenterologists,Medical imaging specialists,AI researchers in healthcare,Developers of medical diagnostic tools 2 months ago

Colon Polyps Detection from Colonoscopy Images Using Deep Learning

computer-vision › object-detection

📄 Abstract

Abstract: Colon polyps are precursors to colorectal cancer, a leading cause of cancer-related mortality worldwide. Early detection is critical for improving patient outcomes. This study investigates the application of deep learning-based object detection for early polyp identification using colonoscopy images. We utilize the Kvasir-SEG dataset, applying extensive data augmentation and splitting the data into training (80\%), validation (20\% of training), and testing (20\%) sets. Three variants of the YOLOv5 architecture (YOLOv5s, YOLOv5m, YOLOv5l) are evaluated. Experimental results show that YOLOv5l outperforms the other variants, achieving a mean average precision (mAP) of 85.1\%, with the highest average Intersection over Union (IoU) of 0.86. These findings demonstrate that YOLOv5l provides superior detection performance for colon polyp localization, offering a promising tool for enhancing colorectal cancer screening accuracy.

Key Contributions

This study applies deep learning object detection, specifically YOLOv5 variants, to detect colon polyps in colonoscopy images using the Kvasir-SEG dataset. It demonstrates that YOLOv5l achieves a high mAP of 85.1%, offering a promising tool for enhancing early detection and accuracy in colorectal cancer screening.

Business Value

Significantly improves the accuracy and efficiency of colorectal cancer screening by enabling earlier and more reliable detection of polyps, potentially saving lives and reducing healthcare costs.

Paper Metadata

Innovation Type

Application

Deployment Feasibility

High. YOLOv5 is a well-established and efficient object detection model, suitable for integration into endoscopy systems or post-procedure analysis tools.

Limitations Addressed

Need for early and accurate detection of colon polyps,Improving the accuracy of automated polyp identification

Performance Gains

YOLOv5l achieved a mean average precision (mAP) of 85.1% and an average IoU of 0.86, outperforming other YOLOv5 variants.

Technical Tags

colon polyp detectiondeep learningobject detectioncolonoscopy imagesYOLOv5Kvasir-SEG datasetdata augmentationmean average precision (mAP)Intersection over Union (IoU)colorectal cancer screening

Research Topics

Medical Image AnalysisObject DetectionDeep Learning for HealthcareCancer ScreeningComputer Vision Applications

Methods & Architectures

Deep learning object detectionYOLOv5 architecture (s, m, l variants)Data augmentationTrain/validation/test split YOLOv5sYOLOv5mYOLOv5l

Applications & Tasks

Medical Imaging Gastroenterology Cancer Screening Endoscopy Object DetectionImage Classification (implicit)Localization Detecting colon polyps in colonoscopy imagesLocalizing polyps for early diagnosisImproving accuracy of colorectal cancer screening

Datasets & Benchmarks

Datasets

Kvasir-SEG dataset

mean average precision (mAP)Intersection over Union (IoU)

Related Fields

Medical ImagingComputer VisionDeep LearningGastroenterologyOncology

Keywords

colon polypsdetectioncolonoscopydeep learningYOLOv5object detectionKvasir-SEGcancer screeningmAPIoUmedical imaging

Academic Context

#Medical Image Analysis#Object Detection#Deep Learning for Healthcare#Cancer Screening#Computer Vision Applications

Technology Stack

Frameworks & Libraries

YOLOv5

Commercial Potential

Potential Products

AI-assisted colonoscopy systemsSoftware for automated analysis of colonoscopy videosTools for early colorectal cancer detection

Target Industries

HealthcareMedical DevicesDiagnostics

Use Case Examples

Real-time polyp detection during colonoscopiesAutomated review of colonoscopy footage for missed polypsAssisting clinicians in identifying suspicious lesions

Competitive Edge

Provides a highly accurate and efficient deep learning-based solution for colon polyp detection, leveraging state-of-the-art object detection architectures like YOLOv5.

Market Opportunity

Large market for cancer screening and diagnostic tools.

Revenue Models

Licensing of the detection algorithmintegration into medical devicesSaaS for analysis.

Resource Requirements

Compute Needs

Moderate for training; efficient for inference, suitable for real-time applications.

Data Requirements

Requires a large, annotated dataset of colonoscopy images (like Kvasir-SEG).

Deployment Constraints

Integration into clinical workflows requires validation, user training, and potential regulatory approvals.

Scalability

YOLOv5 models are known for their efficiency and scalability across different hardware platforms.

Regulatory Considerations

High. Medical diagnostic tools require regulatory approval (e.g.FDA) and adherence to medical device standards.

Production Readiness

Maturity Level

Research/Development

Time to Market

2-4 years for integration into clinical systems and regulatory approval.

Patent Potential

Low, as it's an application of existing models to a specific problem.

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