Lung Nodule Segmentation and Recognition Algorithm Based on Multiposition U-Net

Zhang, Na; Lin, Jianping; Hui, Bengang; Qiao, Bowei; Yang, Weibo; Shang, Rongxin; Wang, Xiaoping; Lei, Jie

doi:10.1155/2022/5112867

Cited by 6 publications

(5 citation statements)

References 31 publications

(40 reference statements)

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“…Lung cancer is the leading cause of mortality in the world [1], affecting both men and women equally. According to the American Cancer Society, 222,500 [2] new instances of lung cancer were diagnosed in 2020 and 155,870 individuals died as a result of the disease. The survival rate for colon cancer is 65.4%, whereas breast cancer has a survival rate of 90.35% and prostate cancer has a survival rate of 99.6% which is much lower than the overall survival rate of 65.4% [3].…”

Section: Introductionmentioning

confidence: 99%

Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution燤odel

Bhattacharyya¹,

Joshua²,

Rao³

et al. 2023

Computers, Materials &Amp; Continua

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Lung cancer is the leading cause of mortality in the world affecting both men and women equally. When a radiologist just focuses on the patient's body, it increases the amount of strain on the radiologist and the likelihood of missing pathological information such as abnormalities are increased. One of the primary objectives of this research work is to develop computerassisted diagnosis and detection of lung cancer. It also intends to make it easier for radiologists to identify and diagnose lung cancer accurately. The proposed strategy which was based on a unique image feature, took into consideration the spatial interaction of voxels that were next to one another. Using the U-NET+Three parameter logistic distribution-based technique, we were able to replicate the situation. The proposed technique had an average Dice co-efficient (DSC) of 97.3%, a sensitivity of 96.5% and a specificity of 94.1% when tested on the Luna-16 dataset. This research investigates how diverse lung segmentation, juxta pleural nodule inclusion, and pulmonary nodule segmentation approaches may be applied to create Computer Aided Diagnosis (CAD) systems. When we compared our approach to four other lung segmentation methods, we discovered that ours was the most successful. We employed 40 patients from Luna-16 datasets to evaluate this. In terms of DSC performance, the findings demonstrate that the suggested technique outperforms the other strategies by a significant margin.

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Section: Introductionmentioning

confidence: 99%

Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution燤odel

Bhattacharyya¹,

Joshua²,

Rao³

et al. 2023

Computers, Materials &Amp; Continua

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“…Deep learning utilizes neural networks to train huge amounts of information, effectively International Journal of Intelligent Engineering and Systems, Vol. 16 learn the nodule features in lower grades and form higher-grade features to segment and predict the medical images [13]. The contribution of this proposed paper is specified as follows:…”

Section: Introductionmentioning

confidence: 99%

Lung Nodule Segmentation Using Cat Swarm Optimization Based Recurrent Neural Network

2023

IJIES

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Nodule segmentation in lung computed tomography (CT) images is a significant part of the detection and diagnosis of lung cancer. Automatic analysis of lung CT images is necessary to calculate lung nodule characteristics for recognizing malignancy. In recent years, deep learning and neural networks have been used in medical applications. Deep learning utilizes neural networks to train huge amounts of information which effectively learns the nodule features in lower to higher grades for segmenting and predicting the medical images. In this paper, the proposed cat swarm optimization (CSO) based recurrent neural network (RNN) is utilized for lung nodule segmentation. The proposed model is estimated on the freely accessible lung image database consortium and image database resource initiative (LIDC-IDRI) dataset. The proposed model is segmented by using the markov random field (MRF) based firefly algorithm (FA) and cuckoo search algorithm (CSA). The result shows that the proposed CSO based RNN model delivers performance metrics like dice coefficient (DC) loss and accuracy values of about 96.28% and 90.28% respectively, which ensures accurate nodule segmentation in lung CT images.

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“…The major contributions of Zhang et al [ 10 ] focused on three parts, i.e., lung parenchyma segmentation, extraction of lung nodule regions, and sign classification based on CT image morphological features. The U-shape network (U-Net) paradigm was utilized to accomplish these three tasks.…”

Section: Introductionmentioning

confidence: 99%

“…The U-shape network (U-Net) paradigm was utilized to accomplish these three tasks. In the lung parenchyma segmentation procedure, the attention mechanism was applied to prevent the background pixel interferences and ameliorate the semantic segmentation accuracy of the U-Net [ 10 ]. Then, the regions of interest can be localized from the receptive fields with the dense atrous convolution approach.…”

Section: Introductionmentioning

confidence: 99%

“…A two-way enhanced feature pyramid network was employed to integrate the low-level pixel features and high-level semantic features through the two-way cross-scale connections. The classification results indicated that the unified U-Net method proposed by Zhang et al [ 10 ] may achieve the best sensitivity (91%) and specificity (88%) values and the excellent receiver operating characteristic curve, in comparison with the other traditional machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computational Methods for Physiological Signal Processing and Data Analysis

Krishnan

Ghoraani

2022

Computational and Mathematical Methods in Medicine

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Biomedical signal processing and data analysis play pivotal roles in the advanced medical expert system solutions. Signal processing tools are able to diminish the potential artifact effects and improve the anticipative signal quality. Data analysis techniques can assist in reducing redundant data dimensions and extracting dominant features associated with pathological status. Recent computational methods have greatly improved the effectiveness of signal processing and data analysis, to support the efficient point-of-care diagnosis and accurate medical decision-making. This editorial article highlights the research works published in the special issue of Computational Methods for Physiological Signal Processing and Data Analysis. The context introduces three deep learning applications in epileptic seizure detection, human exercise intensity analysis, and lung nodule CT image segmentation, respectively. The article also summarizes the research works on detection of event-related potential in the single-trial electroencephalogram (EEG) signals during the auditory tests, along with the methodology on estimating the generalized exponential distribution parameters using the simulated and real data produced under the Type I generalized progressive hybrid censoring schemes. The article concludes with perspectives and discussions on future trends in biomedical signal processing and data analysis technologies.

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Lung Nodule Segmentation and Recognition Algorithm Based on Multiposition U-Net

Cited by 6 publications

References 31 publications

Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution燤odel

Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution燤odel

Lung Nodule Segmentation Using Cat Swarm Optimization Based Recurrent Neural Network

Computational Methods for Physiological Signal Processing and Data Analysis

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