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Related Experiment Video

Updated: Jul 15, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and

Md Faysal Ahamed¹, Md Khalid Syfullah², Ovi Sarkar²

¹Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.

Sensors (Basel, Switzerland)

|September 28, 2023

Summary

This summary is machine-generated.

An AI model, IRv2-Net, accurately detects colorectal polyps in medical images, improving early diagnosis of colorectal cancer. This automated system reduces missed anomalies, aiding endoscopists and enhancing patient care.

Keywords:

CVC-ClinicDB IRv2-Net Kvasir-SEG colonoscopy polyps segmentation test time augmentation

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Colorectal polyps are precancerous growths that can develop into colorectal cancer.
Manual segmentation of polyps is time-consuming, error-prone, and leads to missed diagnoses.
Automated polyp detection systems are needed to improve diagnostic accuracy and efficiency.

Purpose of the Study:

To develop and evaluate a deep learning model for automated polyp segmentation in medical images.
To compare the proposed model's performance against state-of-the-art methods.
To create a user-friendly interface for real-time polyp detection.

Main Methods:

The IRv2-Net model, utilizing a UNet architecture with an InceptionResNetV2 encoder, was developed.
Test Time Augmentation (TTA) was employed for enhanced boundary and multi-scale feature extraction.
The model was evaluated on Kvasir-SEG and CVC-ClinicDB datasets using metrics like accuracy, DSC, IoU, precision, and recall.

Main Results:

The IRv2-Net model achieved superior performance on unseen data, outperforming SOTA models.
It demonstrated high accuracy, Dice Similarity Coefficients (DSC), and Intersection over Union (IoU).
The model successfully detected various polyp types and showed excellent results across datasets, minimizing missed detections.

Conclusions:

The proposed IRv2-Net model offers an effective automated solution for polyp segmentation, crucial for colorectal cancer diagnosis.
The system's real-time capabilities and accuracy have significant potential for clinical colonoscopy procedures.
Further research can build upon this model to enhance early detection and patient outcomes.