Deep neural network approaches for detecting gastric polyps in endoscopic images

DURAK S., BAYRAM B., BAKIRMAN T., ERKUT M., Doğan M., GÜRTÜRK M., ...Daha Fazla

Medical and Biological Engineering and Computing, cilt.59, ss.1563-1574, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 59
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1007/s11517-021-02398-8
  • Dergi Adı: Medical and Biological Engineering and Computing
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, Business Source Elite, Business Source Premier, CINAHL, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE
  • Sayfa Sayıları: ss.1563-1574
  • Anahtar Kelimeler: Deep learning, YOLOv4, Gastric polyp, Gastrointestinal endoscopy, CAD system, ARTIFICIAL-INTELLIGENCE, CANCER
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

© 2021, International Federation for Medical and Biological Engineering.Gastrointestinal endoscopy is the primary method used for the diagnosis and treatment of gastric polyps. The early detection and removal of polyps is vitally important in preventing cancer development. Many studies indicate that a high workload can contribute to misdiagnosing gastric polyps, even for experienced physicians. In this study, we aimed to establish a deep learning–based computer-aided diagnosis system for automatic gastric polyp detection. A private gastric polyp dataset was generated for this purpose consisting of 2195 endoscopic images and 3031 polyp labels. Retrospective gastrointestinal endoscopy data from the Karadeniz Technical University, Farabi Hospital, were used in the study. YOLOv4, CenterNet, EfficientNet, Cross Stage ResNext50-SPP, YOLOv3, YOLOv3-SPP, Single Shot Detection, and Faster Regional CNN deep learning models were implemented and assessed to determine the most efficient model for precancerous gastric polyp detection. The dataset was split 70% and 30% for training and testing all the implemented models. YOLOv4 was determined to be the most accurate model, with an 87.95% mean average precision. We also evaluated all the deep learning models using a public gastric polyp dataset as the test data. The results show that YOLOv4 has significant potential applicability in detecting gastric polyps and can be used effectively in gastrointestinal CAD systems. Graphical abstract: Gastric Polyp Detection Process using Deep Learning with Private Dataset [Figure not available: see fulltext.].