Deep convolutional neural network-based anomaly detection for organ classification in gastric X-ray examination

• Published in: Computers in biology and medicine Vol. 123; p. 103903
• Main Authors: Togo, Ren, Watanabe, Haruna, Ogawa, Takahiro, Haseyama, Miki
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.08.2020; Elsevier Limited
• Subjects: Anomalies; Anomaly detection; Artificial neural networks; Autoencoder; Classification; Deep learning; Esophagus; Gastric X-ray examination; Internal Medicine; Medical image analysis; Neural networks; Other; Probabilistic models; Stomach; Support vector machines; Deep learning; Stomach; Autoencoder; Medical image analysis; Gastric X-ray examination; Anomaly detection; Esophagus

The aim of this study was to determine whether our deep convolutional neural network-based anomaly detection model can distinguish differences in esophagus images and stomach images obtained from gastric X-ray examinations. A total of 6012 subjects were analyzed as our study subjects. Since the numb...