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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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.