MrgaNet: Multi-scale recursive gated aggregation network for tracheoscopy images

IF 4.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Image and Vision Computing Pub Date : 2025-03-21 DOI:10.1016/j.imavis.2025.105503

Ying Wang , Yun Tie , Dalong Zhang , Fenghui Liu , Lin Qi

引用次数: 0

Abstract

Lung cancer is a potentially fatal disease worldwide, and improving the accuracy of diagnosis plays a key role in enhancing patient outcomes. In this study, we extended computer-aided work to the task of assisting tracheoscopy in predicting lung cancer subtypes. To solve the problem of information fusion in different spatial scales and channels, we proposed MrgaNet. The network enhances classification performance by expanding interactions from low to high orders, dynamically adjusting feature weights, and incorporating a channel competition operator for efficient feature selection. Our network achieved a precision of 0.87 in the endobronchial dataset. In addition, the accuracy of 89.25% and 96.76% was achieved in the Kvasir-v2 dataset and the Kvasir-Capsule dataset, respectively. The results demonstrate that MrgaNet achieves superior performance compared to existing excellent methods.

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MrgaNet：气管镜图像的多尺度递归门控聚集网络

肺癌在世界范围内是一种潜在的致命疾病，提高诊断的准确性在提高患者预后方面起着关键作用。在这项研究中，我们将计算机辅助工作扩展到辅助气管镜预测肺癌亚型的任务。为了解决不同空间尺度和通道的信息融合问题，我们提出了MrgaNet。该网络通过将交互从低阶扩展到高阶，动态调整特征权重，并结合通道竞争算子进行有效的特征选择来提高分类性能。我们的网络在支气管内数据集中实现了0.87的精度。Kvasir-v2数据集和Kvasir-Capsule数据集的准确率分别达到89.25%和96.76%。结果表明，与现有的优秀方法相比，MrgaNet取得了卓越的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Image and Vision Computing 工程技术-工程：电子与电气

CiteScore

8.50

自引率

8.50%

发文量

143

审稿时长

7.8 months

期刊介绍： Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.