Can Programmable Gradient Information Enhance Polyp Segmentation?
Colorectal cancer ranks as the third leading cause of cancer-related deaths globally, with early detection and removal of polyps crucial in reducing mortality. Despite colonoscopy being an effective diagnostic tool, up to 28% of polyps are missed due to operator variability. Automated polyp segmenta...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10752496/ |
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| author | Manas Mehta C. Gunavathi Nevin Mathews D. Ruby |
| author_facet | Manas Mehta C. Gunavathi Nevin Mathews D. Ruby |
| author_sort | Manas Mehta |
| collection | DOAJ |
| description | Colorectal cancer ranks as the third leading cause of cancer-related deaths globally, with early detection and removal of polyps crucial in reducing mortality. Despite colonoscopy being an effective diagnostic tool, up to 28% of polyps are missed due to operator variability. Automated polyp segmentation algorithms provide a solution to enhance diagnostic accuracy. However, challenges such as boundary pixel misdetection, varying polyp sizes, and poor model generalization remain prevalent. In this paper, we propose a novel polyp segmentation approach utilizing the YOLOv9 model and its training strategy called Programmable Gradient Information. The study evaluates whether these innovations outperform state-of-the-art polyp segmentation models and introduce a new direction for future architectures. This study addresses the challenges and significantly outperforms state-of-the-art models across five widely-used public datasets. The YOLOv9 based PGI approach exhibits remarkable generalization ability, and demonstrates superior performance, achieving mDice scores of 0.932 on CVC-300, 0.866 on CVC-ColonDB, 0.889 on ETIS-Larib, and 0.919 on KvasirSEG. The model’s high inference speed of 42.3 FPS, make it suitable for real-time applications. These results highlight its potential to improve early colorectal cancer detection. |
| format | Article |
| id | doaj-art-61c3eda19ab044e3ad51b25e28ace676 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-61c3eda19ab044e3ad51b25e28ace6762024-11-22T00:01:43ZengIEEEIEEE Access2169-35362024-01-011216927716929010.1109/ACCESS.2024.349699910752496Can Programmable Gradient Information Enhance Polyp Segmentation?Manas Mehta0https://orcid.org/0009-0000-5077-2998C. Gunavathi1https://orcid.org/0000-0002-4996-069XNevin Mathews2https://orcid.org/0009-0008-0542-0406D. Ruby3https://orcid.org/0000-0001-8938-5020School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaColorectal cancer ranks as the third leading cause of cancer-related deaths globally, with early detection and removal of polyps crucial in reducing mortality. Despite colonoscopy being an effective diagnostic tool, up to 28% of polyps are missed due to operator variability. Automated polyp segmentation algorithms provide a solution to enhance diagnostic accuracy. However, challenges such as boundary pixel misdetection, varying polyp sizes, and poor model generalization remain prevalent. In this paper, we propose a novel polyp segmentation approach utilizing the YOLOv9 model and its training strategy called Programmable Gradient Information. The study evaluates whether these innovations outperform state-of-the-art polyp segmentation models and introduce a new direction for future architectures. This study addresses the challenges and significantly outperforms state-of-the-art models across five widely-used public datasets. The YOLOv9 based PGI approach exhibits remarkable generalization ability, and demonstrates superior performance, achieving mDice scores of 0.932 on CVC-300, 0.866 on CVC-ColonDB, 0.889 on ETIS-Larib, and 0.919 on KvasirSEG. The model’s high inference speed of 42.3 FPS, make it suitable for real-time applications. These results highlight its potential to improve early colorectal cancer detection.https://ieeexplore.ieee.org/document/10752496/Computer visiondeep learningpolyp segmentationprogrammable gradient informationYOLOv9 |
| spellingShingle | Manas Mehta C. Gunavathi Nevin Mathews D. Ruby Can Programmable Gradient Information Enhance Polyp Segmentation? IEEE Access Computer vision deep learning polyp segmentation programmable gradient information YOLOv9 |
| title | Can Programmable Gradient Information Enhance Polyp Segmentation? |
| title_full | Can Programmable Gradient Information Enhance Polyp Segmentation? |
| title_fullStr | Can Programmable Gradient Information Enhance Polyp Segmentation? |
| title_full_unstemmed | Can Programmable Gradient Information Enhance Polyp Segmentation? |
| title_short | Can Programmable Gradient Information Enhance Polyp Segmentation? |
| title_sort | can programmable gradient information enhance polyp segmentation |
| topic | Computer vision deep learning polyp segmentation programmable gradient information YOLOv9 |
| url | https://ieeexplore.ieee.org/document/10752496/ |
| work_keys_str_mv | AT manasmehta canprogrammablegradientinformationenhancepolypsegmentation AT cgunavathi canprogrammablegradientinformationenhancepolypsegmentation AT nevinmathews canprogrammablegradientinformationenhancepolypsegmentation AT druby canprogrammablegradientinformationenhancepolypsegmentation |