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Endoscopic image classification algorithm based on Poolformer

Image desmoking is a significant aspect of endoscopic image processing, effectively mitigating visual field obstructions without the need for additional surgical interventions. However, current smoke removal techniques tend to apply comprehensive video enhancement to all frames, encompassing both sm...

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Autores principales: Wang, Huiqian, Wang, Kun, Yan, Tian, Zhou, Hekai, Cao, Enling, Lu, Yi, Wang, Yuanfa, Luo, Jiasai, Pang, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10551176/
https://www.ncbi.nlm.nih.gov/pubmed/37811325
http://dx.doi.org/10.3389/fnins.2023.1273686
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author Wang, Huiqian
Wang, Kun
Yan, Tian
Zhou, Hekai
Cao, Enling
Lu, Yi
Wang, Yuanfa
Luo, Jiasai
Pang, Yu
author_facet Wang, Huiqian
Wang, Kun
Yan, Tian
Zhou, Hekai
Cao, Enling
Lu, Yi
Wang, Yuanfa
Luo, Jiasai
Pang, Yu
author_sort Wang, Huiqian
collection PubMed
description Image desmoking is a significant aspect of endoscopic image processing, effectively mitigating visual field obstructions without the need for additional surgical interventions. However, current smoke removal techniques tend to apply comprehensive video enhancement to all frames, encompassing both smoke-free and smoke-affected images, which not only escalates computational costs but also introduces potential noise during the enhancement of smoke-free images. In response to this challenge, this paper introduces an approach for classifying images that contain surgical smoke within endoscopic scenes. This classification method provides crucial target frame information for enhancing surgical smoke removal, improving the scientific robustness, and enhancing the real-time processing capabilities of image-based smoke removal method. The proposed endoscopic smoke image classification algorithm based on the improved Poolformer model, augments the model’s capacity for endoscopic image feature extraction. This enhancement is achieved by transforming the Token Mixer within the encoder into a multi-branch structure akin to ConvNeXt, a pure convolutional neural network. Moreover, the conversion to a single-path topology during the prediction phase elevates processing speed. Experiments use the endoscopic dataset sourced from the Hamlyn Centre Laparoscopic/Endoscopic Video Dataset, augmented by Blender software rendering. The dataset comprises 3,800 training images and 1,200 test images, distributed in a 4:1 ratio of smoke-free to smoke-containing images. The outcomes affirm the superior performance of this paper’s approach across multiple parameters. Comparative assessments against existing models, such as mobilenet_v3, efficientnet_b7, and ViT-B/16, substantiate that the proposed method excels in accuracy, sensitivity, and inference speed. Notably, when contrasted with the Poolformer_s12 network, the proposed method achieves a 2.3% enhancement in accuracy, an 8.2% boost in sensitivity, while incurring a mere 6.4 frames per second reduction in processing speed, maintaining 87 frames per second. The results authenticate the improved performance of the refined Poolformer model in endoscopic smoke image classification tasks. This advancement presents a lightweight yet effective solution for the automatic detection of smoke-containing images in endoscopy. This approach strikes a balance between the accuracy and real-time processing requirements of endoscopic image analysis, offering valuable insights for targeted desmoking process.
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spelling pubmed-105511762023-10-06 Endoscopic image classification algorithm based on Poolformer Wang, Huiqian Wang, Kun Yan, Tian Zhou, Hekai Cao, Enling Lu, Yi Wang, Yuanfa Luo, Jiasai Pang, Yu Front Neurosci Neuroscience Image desmoking is a significant aspect of endoscopic image processing, effectively mitigating visual field obstructions without the need for additional surgical interventions. However, current smoke removal techniques tend to apply comprehensive video enhancement to all frames, encompassing both smoke-free and smoke-affected images, which not only escalates computational costs but also introduces potential noise during the enhancement of smoke-free images. In response to this challenge, this paper introduces an approach for classifying images that contain surgical smoke within endoscopic scenes. This classification method provides crucial target frame information for enhancing surgical smoke removal, improving the scientific robustness, and enhancing the real-time processing capabilities of image-based smoke removal method. The proposed endoscopic smoke image classification algorithm based on the improved Poolformer model, augments the model’s capacity for endoscopic image feature extraction. This enhancement is achieved by transforming the Token Mixer within the encoder into a multi-branch structure akin to ConvNeXt, a pure convolutional neural network. Moreover, the conversion to a single-path topology during the prediction phase elevates processing speed. Experiments use the endoscopic dataset sourced from the Hamlyn Centre Laparoscopic/Endoscopic Video Dataset, augmented by Blender software rendering. The dataset comprises 3,800 training images and 1,200 test images, distributed in a 4:1 ratio of smoke-free to smoke-containing images. The outcomes affirm the superior performance of this paper’s approach across multiple parameters. Comparative assessments against existing models, such as mobilenet_v3, efficientnet_b7, and ViT-B/16, substantiate that the proposed method excels in accuracy, sensitivity, and inference speed. Notably, when contrasted with the Poolformer_s12 network, the proposed method achieves a 2.3% enhancement in accuracy, an 8.2% boost in sensitivity, while incurring a mere 6.4 frames per second reduction in processing speed, maintaining 87 frames per second. The results authenticate the improved performance of the refined Poolformer model in endoscopic smoke image classification tasks. This advancement presents a lightweight yet effective solution for the automatic detection of smoke-containing images in endoscopy. This approach strikes a balance between the accuracy and real-time processing requirements of endoscopic image analysis, offering valuable insights for targeted desmoking process. Frontiers Media S.A. 2023-09-21 /pmc/articles/PMC10551176/ /pubmed/37811325 http://dx.doi.org/10.3389/fnins.2023.1273686 Text en Copyright © 2023 Wang, Wang, Yan, Zhou, Cao, Lu, Wang, Luo and Pang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Wang, Huiqian
Wang, Kun
Yan, Tian
Zhou, Hekai
Cao, Enling
Lu, Yi
Wang, Yuanfa
Luo, Jiasai
Pang, Yu
Endoscopic image classification algorithm based on Poolformer
title Endoscopic image classification algorithm based on Poolformer
title_full Endoscopic image classification algorithm based on Poolformer
title_fullStr Endoscopic image classification algorithm based on Poolformer
title_full_unstemmed Endoscopic image classification algorithm based on Poolformer
title_short Endoscopic image classification algorithm based on Poolformer
title_sort endoscopic image classification algorithm based on poolformer
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10551176/
https://www.ncbi.nlm.nih.gov/pubmed/37811325
http://dx.doi.org/10.3389/fnins.2023.1273686
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