A simple example of GI image classification using CNNs. The features were extracted by convolution layers, and then sent to fully connected layers. The predicted classifi...
Abstract:
Gastrointestinal (GI) disease is one of the most common diseases and primarily examined by GI endoscopy. Recently, deep learning (DL), in particular convolutional neural ...Show MoreMetadata
Abstract:
Gastrointestinal (GI) disease is one of the most common diseases and primarily examined by GI endoscopy. Recently, deep learning (DL), in particular convolutional neural networks (CNNs) have made achievements in GI endoscopy image analysis. This review focuses on the applications of DL methods in the analysis of GI images. We summarized and compared the latest published literature related to the common clinical GI diseases and covers the key applications of DL in GI image detection, classification, segmentation, recognition, location, and other tasks. At the end, we give a discussion on the challenges and the research directions of GI image analysis based on DL in the future.
A simple example of GI image classification using CNNs. The features were extracted by convolution layers, and then sent to fully connected layers. The predicted classifi...
Published in: IEEE Access ( Volume: 7)
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