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View all- Bakkouri IBakkouri S(2024)2MGAS-Net: multi-level multi-scale gated attentional squeezed network for polyp segmentationSignal, Image and Video Processing10.1007/s11760-024-03240-y18:6-7(5377-5386)Online publication date: 10-May-2024
AB-DeepLabv3+: An Encoder-Decoder Method with Attention Mechanism for Polyp Segmentation
Pages 262 - 268
Abstract
A polyp is an abnormal growth that can occur in the colon and can be a precursor to colorectal cancer. Colorectal cancer can be diagnosed and treated earlier when polyps are detected and segmented accurately in medical images. The use of deep learning models for medical image segmentation has shown promising results in recent years. This paper presents a polyp segmentation using the modified version of Deeplabv3+ with an attention mechanism called AB-DeepLabv3+. An attention mechanism enhances the discriminatory power of the network by highlighting informative regions of the input image. In the evaluation of the AB-DeepLabv3+ on the publicly available Kvasir-SEG dataset, it achieved state-of-the-art performance with an overall Dice coefficient of 0.98 and an intersection over union of 0.96. The results demonstrated that the AB-DeepLabv3+ provides accurate and efficient polyp segmentation in colonoscopy images.
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Index Terms
- AB-DeepLabv3+: An Encoder-Decoder Method with Attention Mechanism for Polyp Segmentation
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August 2023
783 pages
ISBN:9798400700224
DOI:10.1145/3607947
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Published: 28 September 2023
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IC3 2023: 2023 Fifteenth International Conference on Contemporary Computing
August 3 - 5, 2023
Noida, India
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View all- Bakkouri IBakkouri S(2024)2MGAS-Net: multi-level multi-scale gated attentional squeezed network for polyp segmentationSignal, Image and Video Processing10.1007/s11760-024-03240-y18:6-7(5377-5386)Online publication date: 10-May-2024
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