Multi-scale and Multi-path Cascaded Convolutional Network for Semantic Segmentation of Colorectal Polyps
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929613209337856 |
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| author | Manan, Malik Abdul Jinchao, Feng Yaqub, Muhammad Ahmed, Shahzad Imran, Syed Muhammad Ali Chuhan, Imran Shabir Khan, Haroon Ahmed |
| author_facet | Manan, Malik Abdul Jinchao, Feng Yaqub, Muhammad Ahmed, Shahzad Imran, Syed Muhammad Ali Chuhan, Imran Shabir Khan, Haroon Ahmed |
| contents | Colorectal polyps are structural abnormalities of the gastrointestinal tract that can potentially become cancerous in some cases. The study introduces a novel framework for colorectal polyp segmentation named the Multi-Scale and Multi-Path Cascaded Convolution Network (MMCC-Net), aimed at addressing the limitations of existing models, such as inadequate spatial dependence representation and the absence of multi-level feature integration during the decoding stage by integrating multi-scale and multi-path cascaded convolutional techniques and enhances feature aggregation through dual attention modules, skip connections, and a feature enhancer. MMCC-Net achieves superior performance in identifying polyp areas at the pixel level. The Proposed MMCC-Net was tested across six public datasets and compared against eight SOTA models to demonstrate its efficiency in polyp segmentation. The MMCC-Net's performance shows Dice scores with confidence intervals ranging between (77.08, 77.56) and (94.19, 94.71) and Mean Intersection over Union (MIoU) scores with confidence intervals ranging from (72.20, 73.00) to (89.69, 90.53) on the six databases. These results highlight the model's potential as a powerful tool for accurate and efficient polyp segmentation, contributing to early detection and prevention strategies in colorectal cancer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02443 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-scale and Multi-path Cascaded Convolutional Network for Semantic Segmentation of Colorectal Polyps Manan, Malik Abdul Jinchao, Feng Yaqub, Muhammad Ahmed, Shahzad Imran, Syed Muhammad Ali Chuhan, Imran Shabir Khan, Haroon Ahmed Image and Video Processing Computer Vision and Pattern Recognition Colorectal polyps are structural abnormalities of the gastrointestinal tract that can potentially become cancerous in some cases. The study introduces a novel framework for colorectal polyp segmentation named the Multi-Scale and Multi-Path Cascaded Convolution Network (MMCC-Net), aimed at addressing the limitations of existing models, such as inadequate spatial dependence representation and the absence of multi-level feature integration during the decoding stage by integrating multi-scale and multi-path cascaded convolutional techniques and enhances feature aggregation through dual attention modules, skip connections, and a feature enhancer. MMCC-Net achieves superior performance in identifying polyp areas at the pixel level. The Proposed MMCC-Net was tested across six public datasets and compared against eight SOTA models to demonstrate its efficiency in polyp segmentation. The MMCC-Net's performance shows Dice scores with confidence intervals ranging between (77.08, 77.56) and (94.19, 94.71) and Mean Intersection over Union (MIoU) scores with confidence intervals ranging from (72.20, 73.00) to (89.69, 90.53) on the six databases. These results highlight the model's potential as a powerful tool for accurate and efficient polyp segmentation, contributing to early detection and prevention strategies in colorectal cancer. |
| title | Multi-scale and Multi-path Cascaded Convolutional Network for Semantic Segmentation of Colorectal Polyps |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.02443 |