Patiño Barrientos, SebastiánSierra-Sosa, DanielGarcía-Zapirain, BegoñaCastillo Olea, CristianElmaghraby, Adel Said2026-03-182026-03-182020-01-10Patino-Barrientos, S., Sierra-Sosa, D., Garcia-Zapirain, B., Castillo-Olea, C., & Elmaghraby, A. (2020). Kudo’s classification for colon polyps assessment using a deep learning approach. Applied Sciences (Switzerland), 10(2). https://doi.org/10.3390/APP1002050110.3390/APP10020501https://hdl.handle.net/20.500.14454/5515Colorectal 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.eng© 2020 by the authors.Colon cancerDeep learningImage processingMedical datasetVGGjournal article2026-03-182076-3417