Feasibility of a deep learning‐based algorithm for automated detection and classification of nasal polyps and inverted papillomas on nasal endoscopic images

Discrimination of nasal cavity mass lesions is a challenging work requiring extensive experience. A deep learning‐based automated diagnostic system may help clinicians to classify nasal cavity mass lesions. We demonstrated the feasibility of a convolutional neural network (CNN)‐based diagnosis system for automatic detection and classification of nasal polyps (NP) and inverted papillomas (IP). The trained CNN model appears to reliably classify NP and IP of the nasal cavity from nasal endoscopic images; it also yields a reliable reference for diagnosing nasal cavity mass lesions during nasal endoscopy. However, further studies with more test data are warranted to improve the diagnostic accuracy of our CNN model.