1. Automatic colon polyp detection using Convolutional encoder-decoder model
- Author
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Daniel Sierra-Sosa, Ornela Bardhi, Adel Elmaghraby, and Begonya Garcia-Zapirain
- Subjects
medicine.diagnostic_test ,Colorectal cancer ,Computer science ,business.industry ,Deep learning ,Colonoscopy ,Convolutional neural network ,Pattern recognition ,02 engineering and technology ,medicine.disease ,digestive system diseases ,Colon cancer ,3. Good health ,030218 nuclear medicine & medical imaging ,Colon polyps ,03 medical and health sciences ,0302 clinical medicine ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,020201 artificial intelligence & image processing ,Artificial intelligence ,Encoder decoder ,business - Abstract
Colorectal cancer is a leading cause of cancer deaths, estimated 696 thousand worldwide. Recent years have seen an increase of deep learning techniques and algorithms being used to detect colon polyps. In this work we address colon polyp detection using Convolutional Neural Networks (CNNs) combined with Autoencoders. We use 3 publicly available databases namely: CVC-ColonDB, CVC-ClinicDB and ETIS-LaribPolypDB, to train the model. The results obtained in terms of accuracy are: 0.937, 0.951, 0.967 for the above-mentioned databases respectively. Due to the nature of the colon polyps, diverse shapes and characteristics, there is still place for improvements.
- Published
- 2017
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