AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation

Banu, Syeda Furruka; Sarker, Md. Mostafa Kamal; Abdel-Nasser, Mohamed; Puig, Domènec; Raswan, Hatem A.

doi:10.3390/app112110132

Cited by 20 publications

(8 citation statements)

References 36 publications

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“…In the study by Zhang et al a two-step U-Net CNN segmentation method was proposed for different types of lung nodes in computed tomography images, providing a similar coefficient for Dice (0.8623) in division algorithms [42]. Banu et al showed a fully automated deep-learning framework, including models of lung nodule detection and segmentation, and their results showed that their proposed model achieved a Dice score of 89.79% and 90.35%, and an intersection on the alliance of 82.34% and 83.21% [43]. On the other hand, Monkam et al showed that the overall performance of CNN models depended significantly on the number of convolution layers and the size of the patches, and also revealed that the CNN model with two layers of convolution could have the best performance with 88.28% accuracy, 0.87 AUC, 83.45% F score, and 83.82% sensitivity [44].…”

Section: Discussionmentioning

confidence: 99%

An Effective Approach for Automated Lung Node Detection using CT Scans

Moragheb¹

2022

J Biomed Phys Eng

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Background: Pulmonary or benign nodules are classified as nodules with a diameter of 3 cm or less and defined as non-cancerous nodules. The early diagnosis of malignant lung nodules is important for a more reliable prognosis of lung cancer and less invasive chemotherapy and radiotherapy procedures.Objective: This study aimed to introduce an improved hybrid approach for efficient nodule mask generation and false-positive reduction. Material and Methods:In this experimental study, nodule segmentation preprocessing was conducted to prepare the input computed tomography (CT) scans for the U-Net convolutional neural network (CNN) model, and includes the normalization of CT scans and transfer of pixel values corresponding to the radiodensity of Hounsfield Units (HU). A U-Net CNN was developed based on lung CT scans for nodule identification. Results:The U-net model converged to a dice coefficient of 0.678 with a sensitivity of 75%. Many false positives were considered in every real positive, at 11.1, reduced in the proposed CNN to 2.32 FPs (False Positive) per TP (True Positive). Conclusion:Based on the disadvantages of the largest nodule, the similarity of extracted features of the current study with those of others was imperative. The improved hybrid approach introduced was useful for other image classification tasks as expected.

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Section: Discussionmentioning

confidence: 99%

An Effective Approach for Automated Lung Node Detection using CT Scans

Moragheb¹

2022

J Biomed Phys Eng

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“…In this table, three parameters, sensitivity, specificity, and accuracy, are used for evaluation purposes. The quantitative results showed that the purposed technique outperformed the U-Net [37], AWEU-Net [38], 2D U-Net [9], 2D Seg U Det [39], 3D FCN [40], 3D nodule R-CNN [41], 2D AE [42], 2D CNN [43], 2D LGAN [44], and 2D encoder-decoder [45]. The accuracy of purposed method was 99%, which is much better than the other listed methods, as shown in Table 2.…”

Section: Image Segmentationmentioning

confidence: 91%

Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

et al. 2021

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Over the last two decades, radiologists have been using multi-view images to detect tumors. Computer Tomography (CT) imaging is considered as one of the reliable imaging techniques. Many medical-image-processing techniques have been developed to diagnoses lung cancer at early or later stages through CT images; however, it is still a big challenge to improve the accuracy and sensitivity of the algorithms. In this paper, we propose an algorithm based on image fusion for lung segmentation to optimize lung cancer diagnosis. The image fusion technique was developed through Laplacian Pyramid (LP) decomposition along with Adaptive Sparse Representation (ASR). The suggested fusion technique fragments medical images into different sizes using the LP. After that, the LP is used to fuse the four decomposed layers. For the evaluation purposes of the proposed technique, the Lungs Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) was used. The results showed that the Dice Similarity Coefficient (DSC) index of our proposed method was 0.9929, which is better than recently published results. Furthermore, the values of other evaluation parameters such as the sensitivity, specificity, and accuracy were 89%, 98% and 99%, respectively, which are also competitive with the recently published results.

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“…In 2021, Banu et al [ 82 ] proposed an attention-aware weight excitation U-Net (AWEU-Net) architecture in CT images for lung nodule segmentation. The architecture contains two stages: lung nodule detection based on fine-tuned Faster R-CNN and lung nodule segmentation based on the U-Net with position attention-aware weight excitation (PAWE) and channel attention-aware weight excitation (CAWE).…”

Section: Lung Cancer Prediction Using Deep Learningmentioning

confidence: 99%

Deep Learning Techniques to Diagnose Lung Cancer

Wang

2022

Cancers

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Medical imaging tools are essential in early-stage lung cancer diagnostics and the monitoring of lung cancer during treatment. Various medical imaging modalities, such as chest X-ray, magnetic resonance imaging, positron emission tomography, computed tomography, and molecular imaging techniques, have been extensively studied for lung cancer detection. These techniques have some limitations, including not classifying cancer images automatically, which is unsuitable for patients with other pathologies. It is urgently necessary to develop a sensitive and accurate approach to the early diagnosis of lung cancer. Deep learning is one of the fastest-growing topics in medical imaging, with rapidly emerging applications spanning medical image-based and textural data modalities. With the help of deep learning-based medical imaging tools, clinicians can detect and classify lung nodules more accurately and quickly. This paper presents the recent development of deep learning-based imaging techniques for early lung cancer detection.

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AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation

Cited by 20 publications

References 36 publications

An Effective Approach for Automated Lung Node Detection using CT Scans

An Effective Approach for Automated Lung Node Detection using CT Scans

Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

Deep Learning Techniques to Diagnose Lung Cancer

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