Leveraging YOLOv9 for Advanced Colorectal Polyp Detection

Zeeshan Haider; Ahmad Taher Azar; Samah ALmutlaq

doi:10.48084/etasr.10701

Authors

Zeeshan Haider College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia | Automated Systems and Computing Lab (ASCL), Prince Sultan University, Riyadh, Saudi Arabia
Ahmad Taher Azar College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia | Automated Systems and Computing Lab (ASCL), Prince Sultan University, Riyadh, Saudi Arabia | Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
Samah ALmutlaq College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia | Automated Systems and Computing Lab (ASCL), Prince Sultan University, Riyadh, Saudi Arabia

Volume: 15 | Issue: 5 | Pages: 27629-27639 | October 2025 | https://doi.org/10.48084/etasr.10701

Received: 24 February 2025 | Revised: 20 April 2025 | Accepted: 8 May 2025 | Online: 6 October 2025

Corresponding author: Zeeshan Haider

Abstract

The increasing prevalence of colorectal cancer has necessitated improved diagnostic tools, which has spurred significant research efforts into Artificial Intelligence (AI)-assisted polyp detection and localization methods. Missed diagnoses due to human factors, such as fatigue or inexperience, are recognized to have severe consequences. This study investigates the efficacy of state-of-the-art object detection models for enhanced polyp identification, focusing on the performance of four variants of the YOLOv9 model (gelan-e, gelan-c, yolov9-c, and yolov9-e) for colorectal polyp detection and localization. These models were trained and tested using two distinct datasets: a combined dataset comprised of CVC-CLinicDB and Kvasir-SEG, and the LDPolypVideo dataset. The impact of different YOLOv9 architectures on detection accuracy and localization precision is analyzed. The YOLOv9 variants achieved mAP@50 scores up to 99.1% on CVC-ClinicDB (a 16% improvement over YOLOv8), outperforming YOLOv8 and other models, and 55.56% mAP@50 on LDPolypVideo, demonstrating enhanced accuracy and efficiency in colorectal polyp detection. This study highlights the potential of YOLOv9 to enhance the accuracy and efficiency of colorectal polyp detection.

Keywords:

polyp detection, localization, medical imaging, colorectal cancer, deep learning, YOLOv9, artificial intelligence

Downloads

Download data is not yet available.

References

U. Hameed, M. Ur Rehman, A. Rehman, R. Damaševičius, A. Sattar, and T. Saba, "A deep learning approach for liver cancer detection in CT scans," Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol. 11, no. 7, Jan. 2024, Art. no. 2280558.

H. Inbarani H., A. T. Azar, and J. G, "Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm," Electronics, vol. 9, no. 1, Jan. 2020, Art. no. 188.

S. Al-Otaibi, M. Mujahid, A. R. Khan, H. Nobanee, J. Alyami, and T. Saba, "Dual Attention Convolutional AutoEncoder for Diagnosis of Alzheimer’s Disorder in Patients Using Neuroimaging and MRI Features," IEEE Access, vol. 12, pp. 58722–58739, 2024.

P. K. N. Banu, A. T. Azar, and H. H. Inbarani, "Fuzzy firefly clustering for tumour and cancer analysis," International Journal of Modelling, Identification and Control, vol. 27, no. 2, pp. 92–103, Jan. 2017.

S. R. Waheed, N. M. Suaib, M. S. M. Rahim, A. R. Khan, S. A. Bahaj, and T. Saba, "Synergistic Integration of Transfer Learning and Deep Learning for Enhanced Object Detection in Digital Images," IEEE Access, vol. 12, pp. 13525–13536, 2024.

A. Koubaa, A. Ammar, M. Alahdab, A. Kanhouch, and A. T. Azar, "DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications," Sensors, vol. 20, no. 18, Sep. 2020, Art. no. 5240.

H. I. Elshazly, A. M. Elkorany, A. E. Hassanien, and A. T. Azar, "Ensemble classifiers for biomedical data: Performance evaluation," in 2013 8th International Conference on Computer Engineering & Systems (ICCES), Cairo, Egypt, Nov. 2013, pp. 184–189.

J. H. Bond and P. P. C. of the A. C. of Gastroenterology, "Polyp Guideline: Diagnosis, Treatment, and Surveillance for Patients With Colorectal Polyps," Official journal of the American College of Gastroenterology | ACG, vol. 95, no. 11, pp. 3053-3063, Nov. 2000.

Y. Hao, Y. Wang, M. Qi, X. He, Y. Zhu, and J. Hong, "Risk Factors for Recurrent Colorectal Polyps," Gut and Liver, vol. 14, no. 4, pp. 399–411, Jul. 2020.

N. Shussman and S. D. Wexner, "Colorectal polyps and polyposis syndromes," Gastroenterology Report, vol. 2, no. 1, pp. 1–15, Feb. 2014.

"Global burden of cancer attributable to HIV: a worldwide incidence analysis," IARC. https://www.iarc.who.int/cancer-type/colorectal-cancer.

L. L. Marchand, L. R. Wilkens, L. N. Kolonel, J. H. Hankin, and L. C. Lyu, "Associations of Sedentary Lifestyle, Obesity, Smoking, Alcohol Use, and Diabetes with the Risk of Colorectal Cancer1," Cancer Research, vol. 57, no. 21, pp. 4787–4794, Nov. 1997.

K. Hicham, S. Laghmati, B. Cherradi, S. Hamida, and A. Tmiri, "Enhancing Colorectal Polyps Detection using Transfer Learning on DICOM Metadata," Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 19417–19423, Feb. 2025.

K. ELKarazle, V. Raman, P. Then, and C. Chua, "Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques," Sensors, vol. 23, no. 3, Jan. 2023, Art. no. 1225.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, Jun. 2016, pp. 779–788.

C. Y. Wang, I. H. Yeh, and H. Y. Mark Liao, "YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information," in Computer Vision – ECCV 2024, 2025, pp. 1–21.

J. Redmon and A. Farhadi, "YOLO9000: Better, Faster, Stronger," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, Jul. 2017, pp. 6517–6525.

J. Redmon and A. Farhadi, "YOLOv3: An Incremental Improvement." arXiv, Apr. 08, 2018.

A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao, "YOLOv4: Optimal Speed and Accuracy of Object Detection." arXiv, Apr. 23, 2020.

G. Jocher et al., "ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements." Zenodo, Oct. 29, 2020.

C. Y. Wang, I. H. Yeh, and H. Y. M. Liao, "You Only Learn One Representation: Unified Network for Multiple Tasks." arXiv, May 10, 2021.

Z. Ge, S. Liu, F. Wang, Z. Li, and J. Sun, "YOLOX: Exceeding YOLO Series in 2021." arXiv, Aug. 06, 2021.

C. Li et al., "YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications." arXiv, 2022.

C. Y. Wang, A. Bochkovskiy, and H. Y. M. Liao, "YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors," in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, Jun. 2023, pp. 7464–7475.

J. Terven, D. M. Córdova-Esparza, and J. A. Romero-González, "A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS," Machine Learning and Knowledge Extraction, vol. 5, no. 4, pp. 1680–1716, Nov. 2023.

J. B. Nadar, "YOLO-NAS: A Game-Changer in Object Detection with Deci AI’s Neural Architecture Search Technology," Medium.com, May 30, 2023. https://medium.com/aimonks/yolo-nas-a-game-changer-in-object-detection-with-deci-ais-neural-architecture-search-technology-66e41ee9b3a0.

J. Bernal, F. J. Sánchez, G. Fernández-Esparrach, D. Gil, C. Rodríguez, and F. Vilariño, "WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians," Computerized Medical Imaging and Graphics, vol. 43, pp. 99–111, Jul. 2015.

D. Jha et al., "Kvasir-SEG: A Segmented Polyp Dataset," in MultiMedia Modeling, 2020, pp. 451–462.

Y. Ma, X. Chen, K. Cheng, Y. Li, and B. Sun, "LDPolypVideo Benchmark: A Large-Scale Colonoscopy Video Dataset of Diverse Polyps," in Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 2021, pp. 387–396.

I. Pacal et al., "An efficient real-time colonic polyp detection with YOLO algorithms trained by using negative samples and large datasets," Computers in Biology and Medicine, vol. 141, Feb. 2022, Art. no. 105031.

C. Y. Wang, H. Y. Mark Liao, Y. H. Wu, P. Y. Chen, J. W. Hsieh, and I. H. Yeh, "CSPNet: A New Backbone that can Enhance Learning Capability of CNN," in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA, Jun. 2020, pp. 1571–1580.

K. Hu et al., "Colorectal polyp region extraction using saliency detection network with neutrosophic enhancement," Computers in Biology and Medicine, vol. 147, Aug. 2022, Art. no. 105760.

C. Biffi, P. Salvagnini, N. N. Dinh, C. Hassan, P. Sharma, and A. Cherubini, "A novel AI device for real-time optical characterization of colorectal polyps," npj Digital Medicine, vol. 5, no. 1, Jun. 2022, Art. no. 84.

"GI GeniusTM Intelligent Endoscopy Module." https://www.medtronic.com/en-us/healthcare-professionals/products/digestive-gastrointestinal/gastrointestinal-artificial-intelligence/gi-genius-intelligent-endoscopy-module.html.

A. Nogueira-Rodríguez et al., "Real-time polyp detection model using convolutional neural networks," Neural Computing and Applications, vol. 34, no. 13, pp. 10375–10396, Jul. 2022.

A. Ellahyani, I. E. Jaafari, S. Charfi, and M. E. Ansari, "Fine-tuned deep neural networks for polyp detection in colonoscopy images," Personal and Ubiquitous Computing, vol. 27, no. 2, pp. 235–247, Apr. 2023.

S. Grosu et al., "Machine Learning–based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-of-Concept Study," Radiology, vol. 299, no. 2, pp. 326–335, May 2021.

Z. Haider, A. T. Azar, and T. Saba, "Data Augmentation and Optimizer Tuning for Polyp Segmentation," in 2024 International Conference on Control, Automation and Diagnosis (ICCAD), Paris, France, May 2024, pp. 1–6.

C. Ma, H. Jiang, L. Ma, and Y. Chang, "A Real-Time Polyp Detection Framework for Colonoscopy Video," in Pattern Recognition and Computer Vision, 2022, pp. 267–278.

P. Carrinho and G. Falcao, "Highly accurate and fast YOLOv4-based polyp detection," Expert Systems with Applications, vol. 232, Dec. 2023, Art. no. 120834.

T. Yu et al., "An end-to-end tracking method for polyp detectors in colonoscopy videos," Artificial Intelligence in Medicine, vol. 131, Sep. 2022, Art. no. 102363.

G. Polat, E. Işık Polat, K. Kayabay, and A. Temizel, "Polyp Detection in Colonoscopy Images using Deep Learning and Bootstrap Aggregation," in IEEE International Symposium on Biomedical Imaging, Endoscopy Detection and Segmentation Workshop (EndoCV2021), Apr. 2021.

M. Souaidi, S. Lafraxo, Z. Kerkaou, M. El Ansari, and L. Koutti, "A Multiscale Polyp Detection Approach for GI Tract Images Based on Improved DenseNet and Single-Shot Multibox Detector," Diagnostics, vol. 13, no. 4, Jan. 2023, Art. no. 733.

D. Vázquez et al., "A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images," Journal of Healthcare Engineering, vol. 2017, no. 1, 2017, Art. no. 4037190.

N. Tajbakhsh, S. R. Gurudu, and J. Liang, "Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information," IEEE Transactions on Medical Imaging, vol. 35, no. 2, pp. 630–644, Oct. 2016.

J. Silva, A. Histace, O. Romain, X. Dray, and B. Granado, "Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer," International Journal of Computer Assisted Radiology and Surgery, vol. 9, no. 2, pp. 283–293, Mar. 2014.

N. Tishby and N. Zaslavsky, "Deep learning and the information bottleneck principle," in 2015 IEEE Information Theory Workshop (ITW), Jerusalem, Israel, Apr. 2015, pp. 1–5.

Y. Cai et al., "Reversible Column Networks." arXiv, Feb. 01, 2023, https://doi.org/10.48550/arXiv.2212.11696.

Y. Chen et al., "SdAE: Self-distillated Masked Autoencoder," in Computer Vision – ECCV 2022, vol. 13690, S. Avidan, G. Brostow, M. Cissé, G. M. Farinella, and T. Hassner, Eds. Springer Nature Switzerland, 2022, pp. 108–124.

Z. Shen, Z. Liu, J. Li, Y. G. Jiang, Y. Chen, and X. Xue, "Object Detection from Scratch with Deep Supervision," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 2, pp. 398–412, Feb. 2020.

C. Y. Wang, H. Y. M. Liao, and I. H. Yeh, "Designing Network Design Strategies Through Gradient Path Analysis." arXiv, Nov. 09, 2022.

S. Fan et al., "On line detection of defective apples using computer vision system combined with deep learning methods," Journal of Food Engineering, vol. 286, Dec. 2020, Art. no. 110102.

Md. F. Ahamed, Md. R. Islam, Md. Nahiduzzaman, Md. J. Karim, M. A. Ayari, and A. Khandakar, "Automated Detection of Colorectal Polyp Utilizing Deep Learning Methods With Explainable AI," IEEE Access, vol. 12, pp. 78074–78100, 2024.

L. T. T. Hong et al., "Real-time detection of colon polyps during colonoscopy using YOLOv7," Journal of Military Science and Technology, no. CSCE7, pp. 122–134, Dec. 2023.

S. Wang, J. Xie, Y. Cui, and Z. Chen, "Colorectal Polyp Detection Model by Using Super-Resolution Reconstruction and YOLO," Electronics, vol. 13, no. 12, Jan. 2024, Art. no. 2298.

Leveraging YOLOv9 for Advanced Colorectal Polyp Detection

Authors

Abstract

Keywords:

Downloads

References

Downloads

How to Cite

Metrics

License