Vocal cord lesions (VCL) that are not promptly diagnosed and treated may adversely affect patients’ voice quality and speech communication in the short term, diminishing their quality of life. Long-term progression may lead to malignant tumors in the larynx, posing a threat to patients’ health and life. To this end, this study proposes CKAD-Net, a vocal cord lesion prediction model based on category-guided key feature aggregation and adaptive decision-making. The category-guided key feature aggregation mechanism adaptively adjusts the attention distribution across different features for various lesion types, effectively enhancing category-relevant feature representations and improving the model’s predictive performance. The adaptive decision mechanism employs a learnable adaptive weight factor to dynamically weight the prediction results between feature region representations and category-guided discriminative feature representations. This enables adaptive fusion of decision outcomes from both representations, generating more accurate decisions. Finally, this study validated the performance of the CKAD-Net model on coarse-grained and fine-grained vocal cord lesion prediction tasks using the hospital-private dataset VCLScopeData. The experimental results show that CKAD-Net achieves AUC and ACC values of (0.944 ± 0.002 0.940, 0.948, 0.864 ± 0.008 0.848, 0.880 and 0.929 ± 0.003 0.923, 0.935, 0.730 ± 0.015 0.700, 0.759 respectively, demonstrating superior performance compared to other models.
Liu et al. (Thu,) studied this question.