A systematic review of deep learning based image segmentation to detect polyp

Gupta, Mayuri; Mishra, Ashish

doi:10.1007/s10462-023-10621-1

Cited by 7 publications

(2 citation statements)

References 122 publications

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“…The widespread application of deep learning technologies has significantly improved the accuracy of polyp segmentation [ 18 ]. Akbari et al [ 19 ] used FCN to segment polyps, achieving superior performance over traditional segmentation techniques.…”

Section: Related Workmentioning

confidence: 99%

LightCF-Net: A Lightweight Long-Range Context Fusion Network for Real-Time Polyp Segmentation

Ji,

Li,

Liu

et al. 2024

Bioengineering

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Automatically segmenting polyps from colonoscopy videos is crucial for developing computer-assisted diagnostic systems for colorectal cancer. Existing automatic polyp segmentation methods often struggle to fulfill the real-time demands of clinical applications due to their substantial parameter count and computational load, especially those based on Transformer architectures. To tackle these challenges, a novel lightweight long-range context fusion network, named LightCF-Net, is proposed in this paper. This network attempts to model long-range spatial dependencies while maintaining real-time performance, to better distinguish polyps from background noise and thus improve segmentation accuracy. A novel Fusion Attention Encoder (FAEncoder) is designed in the proposed network, which integrates Large Kernel Attention (LKA) and channel attention mechanisms to extract deep representational features of polyps and unearth long-range dependencies. Furthermore, a newly designed Visual Attention Mamba module (VAM) is added to the skip connections, modeling long-range context dependencies in the encoder-extracted features and reducing background noise interference through the attention mechanism. Finally, a Pyramid Split Attention module (PSA) is used in the bottleneck layer to extract richer multi-scale contextual features. The proposed method was thoroughly evaluated on four renowned polyp segmentation datasets: Kvasir-SEG, CVC-ClinicDB, BKAI-IGH, and ETIS. Experimental findings demonstrate that the proposed method delivers higher segmentation accuracy in less time, consistently outperforming the most advanced lightweight polyp segmentation networks.

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Section: Related Workmentioning

confidence: 99%

LightCF-Net: A Lightweight Long-Range Context Fusion Network for Real-Time Polyp Segmentation

Ji,

Li,

Liu

et al. 2024

Bioengineering

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“…Specifically, these architectures encompass Fully Convolutional Networks (FCN) [27], which are adept at processing inputs of arbitrary dimensions and generating pixel-level label predictions for equivalent dimensions. Fully convolutional networks (FCNs) and their variants are frequently used in CNN-based models [28]; however, these FCNs use convolutional layers instead of the fully connected layers found in the original CNNs. As a result, they only require convolutional (subsampling or upsampling) operations.…”

Section: Comparative Experimentsmentioning

confidence: 99%

A Multiscale Attention Segment Network-Based Semantic Segmentation Model for Landslide Remote Sensing Images

Zhou,

Hong,

Cui

et al. 2024

Remote Sensing

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Landslide disasters have garnered significant attention due to their extensive devastating impact, leading to a growing emphasis on the prompt and precise identification and detection of landslides as a prominent area of research. Previous research has primarily relied on human–computer interactions and visual interpretation from remote sensing to identify landslides. However, these methods are time-consuming, labor-intensive, subjective, and have a low level of accuracy in extracting data. An essential task in deep learning, semantic segmentation, has been crucial to automated remote sensing image recognition tasks because of its end-to-end pixel-level classification capability. In this study, to mitigate the disadvantages of existing landslide detection methods, we propose a multiscale attention segment network (MsASNet) that acquires different scales of remote sensing image features, designs an encoder–decoder structure to strengthen the landslide boundary, and combines the channel attention mechanism to strengthen the feature extraction capability. The MsASNet model exhibited an average accuracy of 95.13% on the test set from Bijie’s landslide dataset, a mean accuracy of 91.45% on the test set from Chongqing’s landslide dataset, and a mean accuracy of 90.17% on the test set from Tianshui‘s landslide dataset, signifying its ability to extract landslide information efficiently and accurately in real time. Our proposed model may be used in efforts toward the prevention and control of geological disasters.

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Principles of Machine Learning

Seeram,

Kanade

2024

Artificial Intelligence in Medical Imaging Technology

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A systematic review of deep learning based image segmentation to detect polyp

Cited by 7 publications

References 122 publications

LightCF-Net: A Lightweight Long-Range Context Fusion Network for Real-Time Polyp Segmentation

LightCF-Net: A Lightweight Long-Range Context Fusion Network for Real-Time Polyp Segmentation

A Multiscale Attention Segment Network-Based Semantic Segmentation Model for Landslide Remote Sensing Images

Principles of Machine Learning

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