  Conventional Endoscopy (CE) and Wireless Capsule Endoscopy (WCE) are known
tools for diagnosing gastrointestinal (GI) tract disorders. Localizing frames
provide valuable information about the anomaly location and also can help
clinicians determine a more appropriate treatment plan. There are many
automated algorithms to detect the anomaly. However, very few of the existing
works address the issue of localization. In this study, we present a
combination of meta-learning and deep learning for localizing both endoscopy
images and video. A dataset is collected from 10 different anatomical positions
of human GI tract. In the meta-learning section, the system was trained using
78 CE and 27 WCE annotated frames with a modified Siamese Neural Network (SNN)
to predict the location of one single image/frame. Then, a postprocessing
section using bidirectional long short-term memory is proposed for localizing a
sequence of frames. Here, we have employed feature vector, distance and
predicted location obtained from a trained SNN. The postprocessing section is
trained and tested on 1,028 and 365 seconds of CE and WCE videos using hold-out
validation (50%), and achieved F1-score of 86.3% and 83.0%, respectively. In
addition, we performed subjective evaluation using nine gastroenterologists.
The results show that the computer-aided methods can outperform
gastroenterologists assessment of localization. The proposed method is compared
with various approaches, such as support vector machine with hand-crafted
features, convolutional neural network and the transfer learning-based methods,
and showed better results. Therefore, it can be used in frame localization,
which can help in video summarization and anomaly detection.
