mlsanityData to 7 Oct 2026

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

arXiv 2409.13868 ↗First posted 20 Sep 2024eess.IV cs.CV cs.LGcited by 6

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Abstract (arXiv)

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

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 14 of its 28 references that are in the map. First posted on arXiv 20 Sep 2024.

Builds on

14 of 28 references.

References · 14

  1. 2023NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Spacecited by 88
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  6. 2024Application of Multimodal Fusion Deep Learning Model in Disease Recognitioncited by 12
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  10. 2024Algorithm Research of ELMo Word Embedding and Deep Learning Multimodal Transformer in Image Descriptioncited by 10
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2 more
  1. 2024Enhancing Convolutional Neural Networks with Higher-Order Numerical Difference Methodscited by 8
  2. 2024Enhancing Deep Learning with Optimized Gradient Descent: Bridging Numerical Methods and Neural Network Trainingcited by 7

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Cited by year

Citing papers per year: 2024: 6.

6 papers.

  1. 2024Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generationcited by 13
  2. 2024Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learningcited by 13
  3. 2024Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classificationcited by 11
  4. 2024Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentationcited by 10
  5. 2024Balancing Innovation and Privacy: Data Security Strategies in Natural Language Processing Applicationscited by 8
  6. 2024Automated Genre-Aware Article Scoring and Feedback Using Large Language Modelscited by 7