Next Article in Journal
Tampered and Computer-Generated Face Images Identification Based on Deep Learning
Next Article in Special Issue
Pre-Cancerous Stomach Lesion Detections with Multispectral-Augmented Endoscopic Prototype
Previous Article in Journal
Facile Sonochemical Preparation of Au-ZrO2 Nanocatalyst for the Catalytic Reduction of 4-Nitrophenol
Previous Article in Special Issue
Metal Artifact Reduction in X-ray CT via Ray Profile Correction
Open AccessArticle

Kudo’s Classification for Colon Polyps Assessment Using a Deep Learning Approach

1
Apolo Scientific Computing Center, Universidad EAFIT, Medellín 50035, Colombia
2
Department of Computer Science and Engineering, University of Louisville, KY 40292, USA
3
eVida Research Group, University of Deusto, 48007 Bilbao, Spain
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2020, 10(2), 501; https://doi.org/10.3390/app10020501
Received: 26 November 2019 / Revised: 3 January 2020 / Accepted: 4 January 2020 / Published: 10 January 2020
(This article belongs to the Special Issue Computer-aided Biomedical Imaging 2020: Advances and Prospects)
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. View Full-Text
Keywords: colon cancer; deep learning; image processing; medical dataset; VGG colon cancer; deep learning; image processing; medical dataset; VGG
Show Figures

Figure 1

MDPI and ACS Style

Patino-Barrientos, S.; Sierra-Sosa, D.; Garcia-Zapirain, B.; Castillo-Olea, C.; Elmaghraby, A. Kudo’s Classification for Colon Polyps Assessment Using a Deep Learning Approach. Appl. Sci. 2020, 10, 501.

AMA Style

Patino-Barrientos S, Sierra-Sosa D, Garcia-Zapirain B, Castillo-Olea C, Elmaghraby A. Kudo’s Classification for Colon Polyps Assessment Using a Deep Learning Approach. Applied Sciences. 2020; 10(2):501.

Chicago/Turabian Style

Patino-Barrientos, Sebastian; Sierra-Sosa, Daniel; Garcia-Zapirain, Begonya; Castillo-Olea, Cristian; Elmaghraby, Adel. 2020. "Kudo’s Classification for Colon Polyps Assessment Using a Deep Learning Approach" Appl. Sci. 10, no. 2: 501.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop