Papers › Automatic Polyp Segmentation using U-Net-ResNet50
Automatic Polyp Segmentation using U-Net-ResNet50
Saruar Alam, Nikhil Kumar Tomar, Aarati Thakur, Debesh Jha, Ashish Rauniyar
Polyps are the predecessors to colorectal cancer which is considered as one of the leading causes of cancer-related deaths worldwide. Colonoscopy is the standard procedure for the identification, localization, and removal of colorectal polyps. Due to variability in shape, size, and surrounding tissue similarity, colorectal polyps are often missed by the clinicians during colonoscopy. With the use of an automatic, accurate, and fast polyp segmentation method during the colonoscopy, many colorectal polyps can be easily detected and removed. The ``Medico automatic polyp segmentation challenge'' provides an opportunity to study polyp segmentation and build an efficient and accurate segmentation algorithm. We use the U-Net with pre-trained ResNet50 as the encoder for the polyp segmentation. The model is trained on Kvasir-SEG dataset provided for the challenge and tested on the organizer's dataset and achieves a dice coefficient of 0.8154, Jaccard of 0.7396, recall of 0.8533, precision of 0.8532, accuracy of 0.9506, and F2 score of 0.8272, demonstrating the generalization ability of our model.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Medical Image Segmentation | Medico automatic polyp segmentation challenge (dataset) | UNet-ResNet50 | DSC | 0.8154 | #2 of 2 | Archive leaderboard | report |
| Medical Image Segmentation | Medico automatic polyp segmentation challenge (dataset) | UNet-ResNet50 | Precision | 0.8533 | #2 of 2 | Archive leaderboard | report |
| Medical Image Segmentation | Medico automatic polyp segmentation challenge (dataset) | UNet-ResNet50 | Recall | 0.8533 | #2 of 2 | Archive leaderboard | report |
| Medical Image Segmentation | Medico automatic polyp segmentation challenge (dataset) | UNet-ResNet50 | mIoU | 0.7396 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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