Effective high-to-low-level feature aggregation network for endoscopic image classification

• Published in: International journal for computer assisted radiology and surgery 2022-07, Vol.17 (7), p.1225-1233
• Main Authors: Li, Sheng, Yao, Jiafeng, Cao, Jing, Kong, Xueting, Zhu, Jinhui
• Format: Article
• Language: English
• Subjects: Backbone; Classification; Computer Imaging; Computer Science; Endoscopy; Feature extraction; Health Informatics; Image classification; Imaging; Medical imaging; Medicine; Medicine & Public Health; Original Article; Pattern Recognition and Graphics; Radiology; Surgery; Vision
• ISSN: 1861-6429; 1861-6410; 1861-6429
• DOI: 10.1007/s11548-022-02591-6

Purpose The accuracy improvement in endoscopic image classification matters to the endoscopists in diagnosing and choosing suitable treatment for patients. Existing CNN-based methods for endoscopic image classification tend to use the deepest abstract features without considering the contribution of low-level features, while the latter is of great significance in the actual diagnosis of intestinal diseases. Methods To make full use of both high-level and low-level features, we propose a novel two-stream network for endoscopic image classification. Specifically, the backbone stream is utilized to extract high-level features. In the fusion stream, low-level features are generated by a bottom-up multi-scale gradual integration (BMGI) method, and the input of BMGI is refined by top-down attention learning modules. Besides, a novel correction loss is proposed to clarify the relationship between high-level and low-level features. Results Experiments on the KVASIR dataset demonstrate that the proposed framework can obtain an overall classification accuracy of 97.33% with Kappa coefficient of 95.25%. Compared to the existing models, the two evaluation indicators have increased by 2% and 2.25%, respectively, at least. Conclusion In this study, we proposed a two-stream network that fuses the high-level and low-level features for endoscopic image classification. The experiment results show that the high-to-low-level feature can better represent the endoscopic image and enable our model to outperform several state-of-the-art classification approaches. In addition, the proposed correction loss could regularize the consistency between backbone stream and fusion stream. Thus, the fused feature can reduce the intra-class distances and make accurate label prediction.