Channel-Wise Attention Mechanism in the 3D Convolutional Network for Lung Nodule Detection

Zhu, Xiaoyu; Wang, Xiaohua; Shi, Yueting; Ren, Shiwei; Wang, Weijiang

doi:10.3390/electronics11101600

Cited by 19 publications

(6 citation statements)

References 33 publications

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“…Compared with the 3D approach, although the amount of computation is reduced, the correlation between slices is ignored, while the 3D nature of CT scans is not noticed. Considering these factors, in recent years, 2D-3D or 3D-based input approaches have been adopted by more and more CADe schemes, such as Zhang Guanglu et al [47] and Zhu Xiaoyu et al [48].…”

Section: Comparisonmentioning

confidence: 99%

Deep learning-based lung nodule detection: a review

Zhou

2024

EAI Endorsed Trans e-Learn

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CT scan acquisition is fast and cost-effective and has become the main lung imaging tool. However, the increase in large numbers of CT scans has placed a heavy burden on radiologists; therefore, automated lung nodule detection techniques are needed to reduce the workload of radiologists and computer-aided detection systems are proposed for further accurate diagnosis of the condition. This review provides a comprehensive overview of recent automated lung nodule detection techniques and challenges, etc., as well as a detailed overview and discussion of current research gaps, future developments, and research trends. Relevant articles published in databases such as IEEE Xplore, Science Direct, PubMed, and Web of Science cover research algorithms published from 2014 to 2023, mainly discussing deep learning-based techniques. The schemes presented in these articles, the databases used, the experimental results, and the performance of the algorithms are compared and discussed. This work aims to introduce researchers and readers to the latest techniques and their advances in the detection of lung nodules in the last decade, which will help researchers and radiologists to further understand the latest techniques in this field.

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Section: Comparisonmentioning

confidence: 99%

Deep learning-based lung nodule detection: a review

Zhou

2024

EAI Endorsed Trans e-Learn

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“…Xiaoyu Zhu et al [23] conducted experiments on the LUNA16 dataset and achieved low false positives and better performance in sensitivity. However, the sensitivity at the high FP level drops slightly.…”

Section: Veronica Et Al [16] Utilized Ann For Lung Nodule Identificat...mentioning

confidence: 99%

A Hybrid Optimization Approach for Pulmonary Nodules Segmentation and Classification using Deep CNN

Gedem,

Rumale

2024

EAI Endorsed Trans Perv Health Tech

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Lung Cancer, due to a lower survival rate, is a deadly disease as compared to other cancers. The prior determination of the lung cancer tends to increase the survival rate. Though there are numerous lung cancer detection techniques, they are all insufficient to detect accurate cancer due to variations in the intensity of the CT scan image. For more accuracy in segmentation of CT images, the proposed Elephant-Based Bald Eagle Optimization (EBEO) algorithm is used. This proposed research concentrates on developing a lung nodule detection technique based on Deep learning. To obtain an effective result, the segmentation process will be carried out using the proposed algorithm. Further, the proposed algorithm will be utilized to tune the hyper parameter of the deep learning classifier to increase detection accuracy. It is expected that the proposed state-of-art method will exceed all conventional methods in terms of detection accuracy due to the effectiveness of the proposed algorithm. This survey will be helpful for the healthcare research communities with sufficient knowledge to understand the concepts of the EBEO algorithm and the Deep Convolutional Neural Network for improving the overall human healthcare system.

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“…Li et al [15] applied a method for pulmonary nodule detection using deep convolutional neural networks. Zhu et al [16] introduced a 3D U-shaped residual network with the foundation of end-to-end detection and channel-wise attention mechanisms. The first step is the introduction of an upgraded attention gate (AG) that uses crucial feature dimensions at skip connections for feature propagation to lower the false-positive rate.…”

Section: Lung Nodule Detectionmentioning

confidence: 99%

3DAGNet: 3D Deep Attention and Global Search Network for Pulmonary Nodule Detection

et al. 2023

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In traditional clinical medicine, respiratory physicians or radiologists often identify the location of lung nodules by highlighting targets in consecutive CT slices, which is labor-intensive and easy-to-misdiagnose work. To achieve intelligent detection and diagnosis of CT lung nodules, we designed a 3D convolutional neural network, called 3DAGNet, for pulmonary nodule detection. Inspired by the diagnostic process of lung nodule localization by physicians, the 3DGNet includes a spatial attention and a global search module. A multi-scale cascade module has also been introduced to enhance the model detection using attention enhancement, global information search, and contextual feature fusion. The experimental results showed that the proposed network achieved accurate detection of lung nodule information, and our method achieves a high sensitivity of 88.08% of the average FROC score on the LUNA16 dataset. In addition, ablation experiments also demonstrated the effectiveness of our method.

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Channel-Wise Attention Mechanism in the 3D Convolutional Network for Lung Nodule Detection

Cited by 19 publications

References 33 publications

Deep learning-based lung nodule detection: a review

Deep learning-based lung nodule detection: a review

A Hybrid Optimization Approach for Pulmonary Nodules Segmentation and Classification using Deep CNN

3DAGNet: 3D Deep Attention and Global Search Network for Pulmonary Nodule Detection

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