An effective neural network model for lung nodule detection in CT images with optimal fuzzy model

Veronica, Benita K. J.

doi:10.1007/s11042-020-08618-x

Cited by 21 publications

(10 citation statements)

References 34 publications

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“…The accuracy of the fusion algorithm is then evaluated, and a satisfactory result is returned. In addition, Veronica [ 31 ] used imajust for contrast enhancement of the images. Then the neural networks are used to classify the feature set of the lungs.…”

Section: Methodsmentioning

confidence: 99%

Multi-Focus Image Fusion Based on Convolution Neural Network for Parkinson’s Disease Image Classification

et al. 2021

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Parkinson’s disease (PD) is a common neurodegenerative disease that has a significant impact on people’s lives. Early diagnosis is imperative since proper treatment stops the disease’s progression. With the rapid development of CAD techniques, there have been numerous applications of computer-aided diagnostic (CAD) techniques in the diagnosis of PD. In recent years, image fusion has been applied in various fields and is valuable in medical diagnosis. This paper mainly adopts a multi-focus image fusion method primarily based on deep convolutional neural networks to fuse magnetic resonance images (MRI) and positron emission tomography (PET) neural photographs into multi-modal images. Additionally, the study selected Alexnet, Densenet, ResNeSt, and Efficientnet neural networks to classify the single-modal MRI dataset and the multi-modal dataset. The test accuracy rates of the single-modal MRI dataset are 83.31%, 87.76%, 86.37%, and 86.44% on the Alexnet, Densenet, ResNeSt, and Efficientnet, respectively. Moreover, the test accuracy rates of the multi-modal fusion dataset on the Alexnet, Densenet, ResNeSt, and Efficientnet are 90.52%, 97.19%, 94.15%, and 93.39%. As per all four networks discussed above, it can be concluded that the test results for the multi-modal dataset are better than those for the single-modal MRI dataset. The experimental results showed that the multi-focus image fusion method according to deep learning can enhance the accuracy of PD image classification.

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Section: Methodsmentioning

confidence: 99%

Multi-Focus Image Fusion Based on Convolution Neural Network for Parkinson’s Disease Image Classification

et al. 2021

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“…For decades, several traditional methods such as thresholding, region growing, clustering, distance transformations, and morphological operations [19]- [23], [70]- [73], [83], [100] have been widely used based on handcrafted features for roughly recognizing candidate nodules. El-Regaily et al [73] first applied rule-based approaches, including contrast-enhancing, region growing, rolling-ball algorithm, and morphological operations, to extract lung parenchyma as well as preserve nodules attached to the lung wall.…”

Section: ) Candidate Nodule Detectionmentioning

confidence: 99%

“…In recent years, massive DNN-based methods, specifically various CNNs, have been proposed to enhance classification performance. According to the difference of network structures, we can categorize these networks into advanced off-the-shelf CNNs [24], [51], [54], [67], [76], [78]- [80], [94], [100], [105], [108], and multi-stream heterogeneous CNNs [53], [58], [72], [75], [85], [86]- [88]. Liu et al [76] developed a High Sensitivity and Specificity [80]can be used to improve performance for better classification.…”

Section: ) False Positive Reductionmentioning

confidence: 99%

A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans

Cao

et al. 2020

IEEE Access

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Lung cancer is one of the malignant tumor diseases with the fastest increase in morbidity and mortality, which poses a great threat to human health. Low-Dose Computed Tomography (LDCT) screening has been proved as a practical technique for improving the accuracy of pulmonary nodule detection and classification at early cancer diagnosis, which contributes to mortality reduction. Therefore, with the explosive growth of CT data, it is of great clinical significance to exploit an effective Computer-Aided Diagnosis (CAD) system for radiologists on automatic nodule analysis. In this paper, a comprehensive review of the application and development of CAD systems is presented. The experimental benchmarks for nodule analysis are first described and summarized, covering public datasets of lung CT scans, commonly used evaluation metrics, and various medical competitions. We then introduce the main structure of a CAD system and present some efficient methodologies. Due to the extensive use of Convolutional Neural Network (CNN)based methods in pulmonary nodule investigations recently, we summarized the advantages of CNNs over traditional image processing methods. Besides, we mainly select the CAD systems developed by state-of-theart CNNs with excellent performance and analyze their objectives, algorithms as well as results. Finally, research trends, existing challenges, and future directions in this field are discussed.

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“…For example, Gruetzemacher et al [35] extended U-Net as a 3D model to perform detection and segmentation for pulmonary tumor. Kamal et al [36] and Veronica [37] incorporated dense connection and fuzzy model with U-Net, respectively. However, a weakness of using U-Net structure is that the performance is highly reliant on the shallowest features where the deeper features are not fully used for segmentation, which causes inaccurate prediction for the location and shape of lesions.…”

Section: Introductionmentioning

confidence: 99%

Boundary Restored Network for Subpleural Pulmonary Lesion Segmentation on Ultrasound Images at Local and Global Scales

Zhang

et al. 2020

J Digit Imaging

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To evaluate the application of machine learning for the detection of subpleural pulmonary lesions (SPLs) in ultrasound (US) scans, we propose a novel boundary-restored network (BRN) for automated SPL segmentation to avoid issues associated with manual SPL segmentation (subjectivity, manual segmentation errors, and high time consumption). In total, 1612 ultrasound slices from 255 patients in which SPLs were visually present were exported. The segmentation performance of the neural network based on the Dice similarity coefficient (DSC), Matthews correlation coefficient (MCC), Jaccard similarity metric (Jaccard), Average Symmetric Surface Distance (ASSD), and Maximum symmetric surface distance (MSSD) was assessed. Our dual-stage boundary-restored network (BRN) outperformed existing segmentation methods (U-Net and a fully convolutional network (FCN)) for the segmentation accuracy parameters including DSC (83.45 ± 16.60%), MCC (0.8330 ± 0.1626), Jaccard (0.7391 ± 0.1770), ASSD (5.68 ± 2.70 mm), and MSSD (15.61 ± 6.07 mm). It also outperformed the original BRN in terms of the DSC by almost 5%. Our results suggest that deep learning algorithms aid fully automated SPL segmentation in patients with SPLs. Further improvement of this technology might improve the specificity of lung cancer screening efforts and could lead to new applications of lung US imaging.

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An effective neural network model for lung nodule detection in CT images with optimal fuzzy model

Cited by 21 publications

References 34 publications

Multi-Focus Image Fusion Based on Convolution Neural Network for Parkinson’s Disease Image Classification

Multi-Focus Image Fusion Based on Convolution Neural Network for Parkinson’s Disease Image Classification

A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans

Boundary Restored Network for Subpleural Pulmonary Lesion Segmentation on Ultrasound Images at Local and Global Scales

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