Identification of Digestive Tract Abnormalities on Endoscopic Images
Author:
Affiliation:
1. Federal University of Amazonas,Center for Research and Development in Electronics and Information Technology,Manaus,Brazil
Funder
Samsung
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10373395/10373414/10373486.pdf?arnumber=10373486
Reference14 articles.
1. HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy
2. Gastrointestinal abnormality detection and classification using empirical wavelet transform and deep convolutional neural network from endoscopic images
3. EfficientNet: rethinking model scaling for convolutional neural networks;Tan
4. A self-learning teacher-student framework for gastrointestinal image classification
5. Efficiency of Transfer Learning for Abnormality Detection using Colonoscopy Images: A Critical Analysis
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