DAS-Net: A lung nodule segmentation method based on adaptive dual-branch attention and shadow mapping

Luo, Shichao; Zhang, Jina; Xiao, Ning; Qiang, Yan; Li, Keqin; Zhao, Juanjuan; Meng, Liang; Song, Ping

doi:10.1007/s10489-021-03038-2

Cited by 12 publications

(3 citation statements)

References 33 publications

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“…These modules enable the extraction of features from various receptive fields, leading to improved semantic segmentation performance [6,30]. Benefiting from the U-shaped [23,25,31,32] architecture's ability to complement highfrequency features and maintain category consistency, the semantic features of different levels can be integrated to obtain contexts of varying scales. Thirdly, by focusing on key features and ignoring irrelevant information, attention mechanisms are introduced [15,16,33].…”

Section: Semantic Segmentationmentioning

confidence: 99%

LUNet: An enhanced upsampling fusion network with efficient self-attention for semantic segmentation

Zhou,

Yang

et al. 2024

Preprint

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Semantic segmentation is an essential aspect of many computer vision tasks. Self-attention (SA) based deep learning methods have shown impressive results in semantic segmentation by capturing long-range dependencies and contextual information. However, the standard SA module has high computational complexity, which limits its use in resource-constrained scenarios. This paper proposes a novel LUNet to improve semantic segmentation performance while addressing the computational challenges of SA. The lightweight self-attention plus (LSA++) module is introduced as a lightweight and efficient variant of the SA module. LSA++ uses compact feature representation and local position embedding to significantly reduce computational complexity while surpassing the accuracy of the standard SA module. Furthermore, to address the loss of edge details during decoding, we propose the Enhanced Upsampling Fusion Module (EUP-FM). This module comprises an enhanced upsampling module and a semantic vector-guided fusion mechanism. EUP-FM effectively recovers edge information and improves the precision of the segmentation map. Comprehensive experiments on PASCAL VOC 2012, Cityscapes, COCO, and SegPC 2021 demonstrate that LUNet outperforms all compared methods. It achieves superior runtime performance and accurate segmentation with excellent model generalization ability. The code is available at https://github.com/hbzhou530/LUNet.

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Section: Semantic Segmentationmentioning

confidence: 99%

LUNet: An enhanced upsampling fusion network with efficient self-attention for semantic segmentation

Zhou,

Yang

et al. 2024

Preprint

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“…Lung cancer is one of the most dangerous cancer types with the highest mortality rate in the world. In the early stage, the symptoms of lung cancer are not obvious [1]. Therefore, early and accurate detection of pulmonary nodules is extremely significant to increase the survival rate of lung cancer [2].…”

Section: Introductionmentioning

confidence: 99%

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Naseer¹,

Masood²,

Akram³

et al. 2023

Computers, Materials &Amp; Continua

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Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the lung. It is mostly caused by the instinctive growth of cells in the lung. Lung nodule detection has a significant role in detecting and screening lung cancer in Computed tomography (CT) scan images. Early detection plays an important role in the survival rate and treatment of lung cancer patients. Moreover, pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung cancer. This work proposed an automatic nodule detection method in CT images based on modified AlexNet architecture and Support vector machine (SVM) algorithm namely LungNet-SVM. The proposed model consists of seven convolutional layers, three pooling layers, and two fully connected layers used to extract features. Support vector machine classifier is applied for the binary classification of nodules into benign and malignant. The experimental analysis is performed by using the publicly available benchmark dataset Lung nodule analysis 2016 (LUNA16). The proposed model has achieved 97.64% of accuracy, 96.37% of sensitivity, and 99.08% of specificity. A comparative analysis has been carried out between the proposed LungNet-SVM model and existing stateof-the-art approaches for the classification of lung cancer. The experimental results indicate that the proposed LungNet-SVM model achieved remarkable performance on a LUNA16 dataset in terms of accuracy.

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“…Numerous imaging modalities such as X-Rays, magnetic resonance imaging (MRI) positron emission tomography (PET), and computed tomography (CT) have been applied to detect pulmonary nodules [19]. The researchers are applying deep learning techniques such as CSE-GAN [20], MSU-Net [21], dual-branch residual network(DB-ResNet) [22], 3D-UNet [23], MSDS-U-Net [24], DS-CMSF [25], dual-path lung nodules segmentation based on boundary enhancement and hybrid transformer (DPBET) [26], DAS-NET [27], Lung PAYNet [28], LungNet-SVM [29] to improve the segmentation task in medical images. The mentioned networks apply benchmark U-Net architecture and obtained different level of accuracy but still, there is a need to improve the accuracy of the segmentation process.…”

Section: Introductionmentioning

confidence: 99%

Lung Cancer Classification Using Modified U-Net Based Lobe Segmentation and Nodule Detection

et al. 2023

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Lung cancer is the most common cause of cancer deaths worldwide. Early detection is crucial for successful treatment and increasing patient survival rates. Artificial intelligence techniques can play a significant role in the early detection of lung cancer. Various methods based on machine learning and deep learning approaches are used to detect lung cancer. This research works aims to develop automated methods to accurately identify and classify lung cancer in CT scans by using computational intelligence techniques. The process typically involves lobe segmentation, extracting candidate nodules, and classifying nodules as either cancer or non-cancer. The proposed lung cancer classification uses modified U-Net based lobe segmentation and nodule detection model consisting of three phases. The first phase segments lobe using CT slice and predicted mask using modified U-Net architecture and the second phase extracts candidate nodule using predicted mask and label employing modified U-Net architecture. Finally, the third phase is based on modified AlexNet, and a support vector machine is applied to classify candidate nodules into cancer and non-cancer. The experimental results of the proposed methodology for lobe segmentation, candidate nodule extraction, and classification of lung cancer have shown promising results on the publicly available LUAN16 dataset. The modified AlexNet-SVM classification model achieves 97.98% of accuracy, 98.84% of sensitivity, 97.47% of specificity, 97.53% of precision, and 97.70% of F1 for the classification of lung cancer.

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DAS-Net: A lung nodule segmentation method based on adaptive dual-branch attention and shadow mapping

Cited by 12 publications

References 33 publications

LUNet: An enhanced upsampling fusion network with efficient self-attention for semantic segmentation

LUNet: An enhanced upsampling fusion network with efficient self-attention for semantic segmentation

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Lung Cancer Classification Using Modified U-Net Based Lobe Segmentation and Nodule Detection

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