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Multi-view difficult airway recognition based on discriminant region guidance
Songlin WU, Guangchao ZHANG, Yuan YAO, Bo PENG
Journal of Computer Applications    2025, 45 (10): 3399-3406.   DOI: 10.11772/j.issn.1001-9081.2024101404
Abstract52)   HTML0)    PDF (2164KB)(25)       Save

Difficult Airway (DA) is a critical preoperative risk factor in clinical surgery, and its accurate recognition faces numerous challenges, such as small dataset size, severe class imbalance, and insufficient single-view recognition capability. Aiming at these issues, a multi-view DA recognition model, DRG-MV-Net (Discriminative Region Guided Multi-View Net), was proposed. In the first stage of the model, the Discriminative Region Guidance Module (DRGM) was employed to detect and emphasize key discriminative regions in facial views automatically using Class Activation Mapping (CAM), thereby generating two types of data augmented images with specific features. In the second stage of the model, features of each view were extracted using ResNet-18 backbone network integrating Dilated-Convolution Block Attention Module (D-CBAM), and multi-view feature integration was performed via the Multi-View Cross Fusion Module (MCFM). Besides, Focal Loss and layered hybrid sampling were combined to mitigate the class imbalance phenomenon. Evaluated results on the constructed clinical dataset demonstrate that the proposed model achieves a G-Mean of 77.22%, an F1-Score of 43.88%, a Matthews Correlation Coefficient (MCC) of 38.73%, and an Area Under the receiver operating Characteristic curve (AUC) of 0.740 7. Compared with the recent DA recognition model MCE-Net (Multi-view Contrastive representation prior and Ensemble classification Network), the proposed model has the G-Mean, F1-Score, and MCC improved by 2.41, 2.34, and 3.41 percentage points, respectively; compared with the baseline model ResNet-18, the proposed model has these metrics improved by 4.85, 6.85, and 8.25 percentage points, respectively, verifying the effectiveness of the proposed model in DA recognition on small, imbalanced datasets and providing new insights and methods for solving complex DA recognition.

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