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Super resolution convolutional neural network based pre-processing for automatic polyp detection in colonoscopy images
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DOI：10.1016/j.compeleceng.2020.106959.png)
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Colonoscopy is the most common methodology used to detect polyps on the colon surface. Increasing the image resolution has the potential to improve the automatic colonoscopy based diagnosis and polyp detection and localization. In this study, we proposed a pre-processing approach that uses convolutional neural network based super resolution method (SRCNN) to increase the resolution of the training colonoscopy images before the localization of polyps. We also investigated the use of CNN based models such as the Single Shot MultiBox Detector (SSD) and Faster Regional CNN (RCNN) for real-time polyp detection and localization. Our results showed that using SRCNN method before the training process provides better results in terms of accuracy in both models compared to the low-resolution cases. Furthermore, we reached an F2 score of 0.945 for the correct localization of colon polyps using Faster RCNN with ResNet-101 feature extractor.
Keyword:
Deep learning
Convolutional neural networks
Transfer learning
Super resolution
Colonoscopy
Colon polyp localization
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Image super-resolution using progressive generative adversarial networks for medical image analysis
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