Computer-Assisted Detection of Colonic Polyps Using Improved Faster R-CNN
| Autor: | Jiangyun Li, Jie Zhang, Dedan Chang, Yaojun Hu |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
Lift (data mining) business.industry Applied Mathematics Deep learning Variable size 020206 networking & telecommunications Pattern recognition 02 engineering and technology Convolutional neural network Object detection Feature (computer vision) Scalability 0202 electrical engineering electronic engineering information engineering Preprocessor 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | Chinese Journal of Electronics. 28:718-724 |
| ISSN: | 2075-5597 1022-4653 |
| Popis: | The deficiencies of existing polyp detection methods remain: i) They primarily depend on the manually extracted features and require considerable amounts of preprocessing. ii) Most traditional methods cannot specify the location of the polyps in colonoscopy images, especially for the polyps with variable size. In order to derive the improvement and lift the accuracy, we propose a novel and scalable detection algorithm based on deep neural networks-an improved Faster Regionbased Convolutional neural networks (Faster R-CNN)-by increasing the fusion of feature maps at different levels. It can be employed to detect and locate polyps, and even achieve a multi-object task for polyps in the future. The experimental consequences demonstrate that the best version among improved algorithms achieves 97.13% accuracy on the CVC-ClinicDB database, overtaking the previous methods. |
| Databáze: | OpenAIRE |
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