Real-time polyp detection model using convolutional neural networks
-
Published:2021-09-21
Issue:
Volume:
Page:
-
ISSN:0941-0643
-
Container-title:Neural Computing and Applications
-
language:en
-
Short-container-title:Neural Comput & Applic
Author:
Nogueira-Rodríguez AlbaORCID, Domínguez-Carbajales RubénORCID, Campos-Tato FernandoORCID, Herrero JesúsORCID, Puga ManuelORCID, Remedios DavidORCID, Rivas LauraORCID, Sánchez EloyORCID, Iglesias Águeda, Cubiella JoaquínORCID, Fdez-Riverola FlorentinoORCID, López-Fernández HugoORCID, Reboiro-Jato MiguelORCID, Glez-Peña DanielORCID
Abstract
AbstractColorectal cancer is a major health problem, where advances towards computer-aided diagnosis (CAD) systems to assist the endoscopist can be a promising path to improvement. Here, a deep learning model for real-time polyp detection based on a pre-trained YOLOv3 (You Only Look Once) architecture and complemented with a post-processing step based on an object-tracking algorithm to reduce false positives is reported. The base YOLOv3 network was fine-tuned using a dataset composed of 28,576 images labelled with locations of 941 polyps that will be made public soon. In a frame-based evaluation using isolated images containing polyps, a general F1 score of 0.88 was achieved (recall = 0.87, precision = 0.89), with lower predictive performance in flat polyps, but higher for sessile, and pedunculated morphologies, as well as with the usage of narrow band imaging, whereas polyp size < 5 mm does not seem to have significant impact. In a polyp-based evaluation using polyp and normal mucosa videos, with a positive criterion defined as the presence of at least one 50-frames-length (window size) segment with a ratio of 75% of frames with predicted bounding boxes (frames positivity), 72.61% of sensitivity (95% CI 68.99–75.95) and 83.04% of specificity (95% CI 76.70–87.92) were achieved (Youden = 0.55, diagnostic odds ratio (DOR) = 12.98). When the positive criterion is less stringent (window size = 25, frames positivity = 50%), sensitivity reaches around 90% (sensitivity = 89.91%, 95% CI 87.20–91.94; specificity = 54.97%, 95% CI 47.49–62.24; Youden = 0.45; DOR = 10.76). The object-tracking algorithm has demonstrated a significant improvement in specificity whereas maintaining sensitivity, as well as a marginal impact on computational performance. These results suggest that the model could be effectively integrated into a CAD system.
Funder
Ministerio de Economía, Industria y Competitividad, Gobierno de España Consellería de Educación, Universidades e Formación Profesional Xunta de Galicia Fundação para a Ciência e a Tecnologia Universidade de Vigo
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Reference68 articles.
1. Cancer today, https://gco.iarc.fr/today/online-analysis-table?v=2020&mode=cancer&mode_population=continents&population=900&populations=900&key=asr&sex=0&cancer=39&type=0&statistic=5&prevalence=0&population_group=0&ages_group%5B%5D=0&ages_group%5B%5D=17&group_cancer=1&include_nmsc=1&include_nmsc_other=1. Accessed 28 Dec 2020 2. US Preventive Services Task Force, Bibbins-Domingo K, Grossman DC, Curry SJ, Davidson KW, Epling JW, García FAR, Gillman MW, Harper DM, Kemper AR, Krist AH, Kurth AE, Landefeld CS, Mangione CM, Owens DK, Phillips WR, Phipps MG, Pignone MP, Siu AL (2016) Screening for colorectal cancer: US preventive services task force recommendation statement. JAMA 315:2564. https://doi.org/10.1001/jama.2016.5989. 3. Cubiella J, González A, Almazán R, Rodríguez-Camacho E, Zubizarreta R, Peña-Rey Lorenzo I (2020) Overtreatment in nonmalignant lesions detected in a colorectal cancer screening program: a cross-sectional analysis. Res Sq. https://doi.org/10.21203/rs.3.rs-113901/v1 4. Zauber AG, Winawer SJ, O’Brien MJ, Lansdorp-Vogelaar I, van Ballegooijen M, Hankey BF, Shi W, Bond JH, Schapiro M, Panish JF, Stewart ET, Waye JD (2012) Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths. N Engl J Med 366:687–696. https://doi.org/10.1056/NEJMoa1100370 5. Wiegering A, Ackermann S, Riegel J, Dietz UA, Götze O, Germer C-T, Klein I (2016) Improved survival of patients with colon cancer detected by screening colonoscopy. Int J Colorectal Dis 31:1039–1045. https://doi.org/10.1007/s00384-015-2501-6
Cited by
40 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|