MIB-ANet: A novel multi-scale deep network for nasal endoscopy-based adenoid hypertrophy grading

Mingmin Bi, Siting Zheng, Xuechen Li, Haiyan Liu, Xiaoshan Feng, Yunping Fan, Linlin Shen

Research output: Journal PublicationArticlepeer-review


Introduction: To develop a novel deep learning model to automatically grade adenoid hypertrophy, based on nasal endoscopy, and asses its performance with that of E.N.T. clinicians. Methods: A total of 3,179 nasoendoscopic images, including 4-grade adenoid hypertrophy (Parikh grading standard, 2006), were collected to develop and test deep neural networks. MIB-ANet, a novel multi-scale grading network, was created for adenoid hypertrophy grading. A comparison between MIB-ANet and E.N.T. clinicians was conducted. Results: In the SYSU-SZU-EA Dataset, the MIB-ANet achieved 0.76251 F1 score and 0.76807 accuracy, and showed the best classification performance among all of the networks. The visualized heatmaps show that MIB-ANet can detect whether adenoid contact with adjacent tissues, which was interpretable for clinical decision. MIB-ANet achieved at least 6.38% higher F1 score and 4.31% higher accuracy than the junior E.N.T. clinician, with much higher (80× faster) diagnosing speed. Discussion: The novel multi-scale grading network MIB-ANet, designed for adenoid hypertrophy, achieved better classification performance than four classical CNNs and the junior E.N.T. clinician. Nonetheless, further studies are required to improve the accuracy of MIB-ANet.

Original languageEnglish
Article number1142261
JournalFrontiers in Medicine
Publication statusPublished - 2023
Externally publishedYes


  • adenoid hypertrophy
  • convolutional neural networks
  • deep learning
  • medical image classification
  • nasal endoscopy

ASJC Scopus subject areas

  • Medicine (all)


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