| Autor: |
Sebastian Patino-Barrientos, Daniel Sierra-Sosa, Begonya Garcia-Zapirain, Cristian Castillo-Olea, Adel Elmaghraby |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Applied Sciences, Vol 10, Iss 2, p 501 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2076-3417 |
| DOI: |
10.3390/app10020501 |
| Popis: |
Colorectal cancer (CRC) is the second leading cause of cancer death in the world. This disease could begin as a non-cancerous polyp in the colon, when not treated in a timely manner, these polyps could induce cancer, and in turn, death. We propose a deep learning model for classifying colon polyps based on the Kudo’s classification schema, using basic colonoscopy equipment. We train a deep convolutional model with a private dataset from the University of Deusto with and without using a VGG model as a feature extractor, and compared the results. We obtained 83% of accuracy and 83% of F1-score after fine tuning our model with the VGG filter. These results show that deep learning algorithms are useful to develop computer-aided tools for early CRC detection, and suggest combining it with a polyp segmentation model for its use by specialists. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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