Research Article
Automatic Detection of Polyps Using Deep Learning
@INPROCEEDINGS{10.1007/978-3-031-60665-6_19, author={Francisco Oliveira and Dalila Barbosa and Ishak Pa\`{e}al and Danilo Leite and Ant\^{o}nio Cunha}, title={Automatic Detection of Polyps Using Deep Learning}, proceedings={Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings}, proceedings_a={MOBIHEALTH}, year={2024}, month={6}, keywords={Machine learning polyp detection colonoscopy YOLO}, doi={10.1007/978-3-031-60665-6_19} }- Francisco Oliveira
Dalila Barbosa
Ishak Paçal
Danilo Leite
António Cunha
Year: 2024
Automatic Detection of Polyps Using Deep Learning
MOBIHEALTH
Springer
DOI: 10.1007/978-3-031-60665-6_19
Abstract
Colorectal cancer is a leading health concern worldwide, with late detection being a primary challenge due to its often-asymptomatic nature. Routine examinations like colonoscopies play a pivotal role in early detection. This study harnesses the potential of Deep Learning, specifically convolutional neural networks, in enhancing the accuracy of polyp detection from medical images. Three distinct models, YOLOv5, YOLOv7, and YOLOv8, were trained on the PICCOLO dataset, a comprehensive collection of polyp images. The comparative analysis revealed YOLOv5’s submodel S as the most efficient, achieving an accuracy of 92.2%, a sensitivity of 69%, an F1 score of 74% and a mAP of 76.8%, emphasizing the effectiveness of these networks in polyp detection.
