DivGI: delve into digestive endoscopy image classification.

Abstract

Purpose: Gastrointestinal (GI) endoscopic imaging involves capturing routine anatomical landmarks and suspected lesions during endoscopic procedures for the clinical diagnosis of GI diseases. These images present three key challenges compared to typical scene images: significant class imbalance, a lack of distinctive features, and high similarity between some categories. While existing research has addressed the issue of image quantity imbalance, the challenges posed by indistinct features and inter-category similarity remain unresolved. This study proposes a unified image classification framework designed to tackle all three of these challenges comprehensively.

Methods: We present a novel network architecture, DivGI, which integrates three essential strategies-balanced sampling, fine-grained classification, and multi-label classification-within a single framework. The balanced sampling strategy is implemented via resampling and mix-up techniques, fine-grained classification is enabled through multi-granularity feature learning, and multi-label classification is achieved using hierarchical label joint learning. The performance of our method is validated using three publicly available datasets.

Results: Extensive experimental results demonstrate that DivGI significantly improves classification accuracy compared to existing approaches, with Matthews correlation coefficients (MCC) of 91.31% on the HyperKvasir dataset, 86.72% on the Upper GI dataset, and 82.88% on the GastroVision dataset. These results highlight that DivGI is more effective and efficient compared to existing methods.

Conclusion: The proposed GI classification network, which incorporates multiple strategies, effectively classifies both routine landmark and suspected lesion images, aiming to facilitate better clinical diagnostics in gastrointestinal endoscopy. The code and data are publicly available at https://github.com/howardchina/DivGI.