Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentation Article Swipe

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Mingxiu Sui , Jiacheng Hu , Tong Zhou , Zibo Liu , Luguang Wen , Junliang Du ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2409.13868

This paper introduces a novel deep-learning method for the automatic detection and segmentation of lung nodules, aimed at advancing the accuracy of early-stage lung cancer diagnosis. The proposed approach leverages a unique "Channel Squeeze U-Structure" that optimizes feature extraction and information integration across multiple semantic levels of the network. This architecture includes three key modules: shallow information processing, channel residual structure, and channel squeeze integration. These modules enhance the model's ability to detect and segment small, imperceptible, or ground-glass nodules, which are critical for early diagnosis. The method demonstrates superior performance in terms of sensitivity, Dice similarity coefficient, precision, and mean Intersection over Union (IoU). Extensive experiments were conducted on the Lung Image Database Consortium (LIDC) dataset using five-fold cross-validation, showing excellent stability and robustness. The results indicate that this approach holds significant potential for improving computer-aided diagnosis systems, providing reliable support for radiologists in clinical practice and aiding in the early detection of lung cancer, especially in resource-limited settings

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Deep Learning

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Artificial Intelligence

Computer Science

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Deep learning Segmentation Artificial intelligence Channel (broadcasting) Nodule (geology) Computer science Pattern recognition (psychology) Geology Telecommunications Paleontology

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2409.13868
PDF: https://arxiv.org/pdf/2409.13868
OA Status: green
Cited By: 1
Related Works: 10
OpenAlex ID: https://openalex.org/W4403752750

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DOI: https://doi.org/10.48550/arxiv.2409.13868

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Title: Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentation

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-09-20

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Authors: Mingxiu Sui, Jiacheng Hu, Tong Zhou, Zibo Liu, Luguang Wen, Junliang Du

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Landing page: https://arxiv.org/abs/2409.13868

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PDF URL: https://arxiv.org/pdf/2409.13868

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Concepts: Deep learning, Segmentation, Artificial intelligence, Channel (broadcasting), Nodule (geology), Computer science, Pattern recognition (psychology), Geology, Telecommunications, Paleontology

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Cited by: 1

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Related works (count): 10

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