Inverted papilloma and nasal polyp classification using a deep convolutional network integrated with an attention mechanism.

Background: Inverted papilloma (IP) is a common sinus neoplasm with a probability of malignant transformation. Nasal polyps (NP) are the most frequent masses in the sinus. The classification of IP and NP using computed tomography (CT) is highly significant for preoperative recognition, treatment, and clinical examination. Few visible differences exist between IP and NP in CT, making it a challenge for otolaryngologists to distinguish between them. This study intended to classify IP and NP using a neural network and analyze its ability to discriminate the differences.

Methods: IP and NP in CT were classified using a deep convolutional neural network (CNN) with an attention mechanism, which combines a densely connected convolutional network (DenseNet) and squeeze-and-excitation network (SENet). Using SENet's channel attention, the specific channel weights in the feature maps are improved, which can enhance feature discriminativeness. To discuss the interpretability of SE-DenseNet, we analyzed the heatmap of the final convolutional layer.

Results: We evaluated the classification performance of SE-DenseNet on a clinical dataset containing 3382 slices for 136 patients. The experimental results and a heatmap show that SE-DenseNet can effectively locate sinonasal lesions in patients and distinguish IP from NP with an average Acc of 88.4% and AUC of 0.87.

Conclusion: Otolaryngologists can use the proposed model to diagnose IP and NP in CT because of its accuracy and efficiency. Moreover, the visualized heatmaps produced by the convolutional layers show that the method is reliable.