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

Journal: International forum of allergy & rhinology
PMID: 34148298

Abstract

BACKGROUND: 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).

Authors

  • Benton Girdler
    Department of Electrical and Computer Engineering, University of Kentucky, Kentucky, USA.
  • Hyun Moon
    Department of Otolaryngology, Gangneung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Republic of Korea.
  • Mi Rye Bae
    Department of Otolaryngology-Head and Neck Surgery, Bundang Jesaeng General Hospital, Seongnam, Republic of Korea.
  • Sung Seok Ryu
    Department of Otorhinolaryngology-Head and Neck Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Jihye Bae
    Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA.
  • Myeong Sang Yu
    Department of Otorhinolaryngology-Head and Neck Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

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