Name
Abstract | ||
|---|---|---|
Colorectal cancer is one of the major causes of cancer deaths worldwide. To achieve early cancer screening, detecting the presence of polyps in the colon tract is the preferred technique. In this paper, a deep learning approach for identifying polyps in colonoscopy images is proposed. The novelty of our technique stems from the fact that it fully employs a pre-trained Convolutional Neural Network (CNN) architecture as a feature extractor. Contrary to the conventional methods which either perform fine-tuning or train the CNN from scratch, we utilize the CNN output features as an input to train the Support Vector Machine (SVM) Classifier. The efficiency of the presented framework is demonstrated on the public CVC ColonDB, in which the experimental results indicate that our methodology significantly outperforms other competitive paradigms. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Automatic polyp detection, Deep learning, CNN, feature extractor |
Field | DocType | ISSN |
Pattern recognition,Computer science,Convolutional neural network,Convolution,Support vector machine,Feature extraction,Artificial intelligence,Deep learning,Novelty,Classifier (linguistics),Cancer screening | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bilal Taha | 1 | 14 | 5.92 |
| Jorge Dias | 2 | 175 | 33.83 |
| N. Werghi | 3 | 74 | 11.38 |