Proposing Novel Data Analytics Method for Anatomical Landmark Identification from Endoscopic Video Frames.

ABSTRACT:

Background

The anatomical landmarks contain the characteristics that are used to guide the gastroenterologists during the endoscopy. The expert can also ensure the completion of examination with the help of the anatomical landmarks. Automatic detection of anatomical landmarks in endoscopic video frames can be helpful for guiding the physicians during screening the gastrointestinal tract (GI).

Method

This study presents an automatic novel method for anatomical landmark detection of GI tract from endoscopic video frames based on semisupervised deep convolutional neural network (CNN) and compares the results with supervised CNN model. We consider the anatomical landmarks from Kvasir dataset that includes 500 images for each class of Z-line, pylorus, and cecum. The resolution of

SUBMITTER: Ayyoubi Nezhad S 

PROVIDER: S-EPMC8890842 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature