Deep learning models for anomaly detection in wireless capsule endoscopy video frames: The transfer learning approach
2021
Wireless Capsule Endoscopy (WCE) is a popular and widely accepted technique
for the examination of gastro-intestinal (GI) tract and small bowel. A small
capsule is swallowed by the patient equipped with a camera that records its
journey in the form of a video. This video helps the doctor in visual
examination of a patient’s GI tract and intestine which further helps in the
diagnosis of diseases. Several image processing and machine learning
techniques have been proposed by the researchers for abnormal frame
detection from WCE videos. Recent approaches have used deep learning
frameworks for abnormality detection. In this study, transfer learning is
employed using various available deep learning models for classification of
normal images and images with abnormality. Three popular models—InceptionV3,
Resnet50, and InceptionResnetV2—were trained and compared. It is found that
the performance deep learning models is quiet better in comparison to
traditional machine learning methods and InceptionV3 outperformed with 93%
accuracy.
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