The Combination of Data-Driven Machine Learning Approaches and Prior Knowledge for Robust Medical Image Processing and Analysis

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Original Research
14 April 2023
MIB-ANet: A novel multi-scale deep network for nasal endoscopy-based adenoid hypertrophy grading
Mingmin Bi
5 more and 
Linlin Shen
(A) The overview of the proposed MIB-ANet architecture. (B) The architecture of ANet. (C) The architecture of Inception Block and Modified Inception Block.

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.

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