Deep Transfer Convolutional Neural Network and Extreme Learning Machine for lung nodule diagnosis on CT images

Huang, Xufeng; Lei, Qiang; Xie, Tingli; Zhang, Yahui; Hu, Zhen; Zhou, Qi

doi:10.1016/j.knosys.2020.106230

Cited by 75 publications

(33 citation statements)

References 44 publications

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“…Huang et al [31] this recommend a diagnostic method based on the CNN deep transmission and extreme learning machine (ELM), which combines the synergy of two methods to manage the classification of naked dead nodes.…”

Section: Problem Methodologymentioning

confidence: 99%

An Early Prediction and Classification of Lung Nodule Diagnosis on CT Images Based on Hybrid Deep Learning Techniques

Gugulothu

Balaji

2022

Preprint

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Detection of malignant lung nodules at an early stage may allow for clinical interventions that increase the survival rate of lung cancer patients. The use of hybrid deep learning techniques to detect nodules will improve the sensitivity of lung cancer screening and the interpretation speed of lung scans.Accurate detection of lung nodes is an important step in computed tomography (CT) imaging to detect lung cancer. However, it is very difficult to identify strong nodes due to the diversity of lung nodes and the complexity of the surrounding environment.Here, we proposed alung nodule detection and classification with CT images based on hybrid deep learning (LNDC-HDL) techniques. First, we introduce achaotic bird swarm optimization (CBSO) algorithm for lung nodule segmentation using statistical information. Second, we illustrate anImproved Fish Bee (IFB) algorithm for feature extraction and selection process. Third, we develop hybrid classifier i.e. hybrid differential evolution based neural network (HDE-NN) for tumor prediction and classification.Experimental results have shown that the use of computed tomography, which demonstrates the efficiency and importance of the HDE-NN specific structure for detecting lung nodes on CT scans, increases sensitivity and reduces the number of false positives. The proposed method shows that the benefits of HDE-NN node detection can be reaped by combining clinical practice.

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Section: Problem Methodologymentioning

confidence: 99%

An Early Prediction and Classification of Lung Nodule Diagnosis on CT Images Based on Hybrid Deep Learning Techniques

Gugulothu

Balaji

2022

Preprint

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“…Broet et al [ 10 ] proposed a statistical scoring method based on microarray data to identify gene expression characteristics that separate the early stage from the late stage. Huang et al [ 11 ] proposed the use of deep transfer convolutional neural networks and extreme learning machines to diagnose lung nodules on CT images. Koike et al [ 12 ] used machine learning‐based models to classify to predict the recurrence of peripheral lung squamous cell carcinoma.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Classification of Lung Cancer Using Deep Transfer Learning Technology and Multi‐Omics Data

Zhu

Dai

Liu

et al. 2021

Chinese J of Electronics

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In recent years, with the increasing application of highthroughput sequencing technology, researchers have obtained and accumulated a large amount of multi‐omics data, making it possible to diagnose cancer at the gene expression level. The proliferation of various omics data can provide a large amount of biological information, which brings new opportunities and great challenges as well to cancer classification and diagnosis. Machine learning algorithms for early diagnosis of lung cancer have emerged that distinguish cancers of the early and late stages by using genomic features. Omics data are generally characterized with low sample size, high dimensionality and high noise. Therefore, simple direct application of common classification methods cannot achieve better performance and must be improved in a targeted manner. This paper puts forward a combined convolutional neural network and convolutional auto‐encoders approach to construct a deep migratory learning classification model for early lung cancer diagnosis. First, the convolutional auto‐encoders algorithm is used to reduce the dimensionality of the dataset in order to make it better meet the requirements of migration learning. Second, a neural network model is constructed with the original dataset and the existing labeled dataset, and the model migration rules are set as well. Finally, a small number of labeled target datasets are used in the training to complete the construction of the classification model. The proposed convolutional neural network method based on model migration and five other popular machine learning models are used to classify and predict the three lung cancer gene datasets and the integrated dataset. The experimental results show that such four evaluation metrics as accuracy, precision, recall, and f1‐score with our proposed method have obtained better prediction performance, and the average area under curve result also shows our proposed method is optimal.

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“…In the direction of training, Huang et al proposed that the lower tibial muscle allograft has the advantages of certain radian and soft muscle surface, which can better restore the shape and function of the respiratory muscle pelvis. However, the preservation and transportation conditions of the lower tibial muscle allograft are strict, and the problems of immune rejection and potential disease transmission need to be further studied [ 6 ]. Zhang et al proposed treating severe muscle respiratory muscle injury; the source of excellent muscle graft materials has become an important topic.…”

Section: Introductionmentioning

confidence: 99%

Multislice Spiral CT Image Analysis and Meta-Analysis of Inspiratory Muscle Training on Respiratory Muscle Function

Ming

et al. 2021

Journal of Healthcare Engineering

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Respiratory muscle function has a significant effect on stroke. Stroke is one of the most common cardiovascular and cerebrovascular diseases in the clinic and has a significant impact on the quality of life of patients. Hemiplegia, cerebral hemorrhage, and even death can occur, mainly in the elderly. In this paper, we meta-analyzed the effect of inspiratory muscle training on respiratory muscle function. In this article, we used a topic search method to search for relevant literature on respiratory muscle training and obtained 58 and 32 literature studies from CNKI and Wanfang Data, respectively. As a result of the screening, 36 and 28 documents were obtained. In this paper, 64 selected articles were studied. The authors make statistics on the literature of designing serum content index and multislice spiral CT (Member of the Society of Cardiological Technicians) image of patients, so as to analyze the influence of CT image and inspiratory muscle training on respiratory muscle function. The study showed that FVC, FEV1, MIP, and diaphragm mobility of the experimental group were significantly improved after treatment in more than 85% of the studies ( P < 0.05 ), while those of the control group were not significantly improved ( P > 0.05 ). The comparison between the two groups after treatment showed that FVC, FEV1, MIP, and diaphragm mobility of the experimental group were higher than those of the control group ( P < 0.05 ). The application of multislice spiral CT image analysis technology can effectively evaluate the effect of inspiratory muscle training on respiratory dysfunction in stroke patients, the mechanism of which regulates the expression of related pathways, suppresses the inflammatory response, and can reduce oxidative stress damage. Therefore, respiratory muscle training can improve the function of respiratory muscle and reduce the death rate of cerebellar hemorrhage in patients with stroke and other vascular diseases.

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Deep Transfer Convolutional Neural Network and Extreme Learning Machine for lung nodule diagnosis on CT images

Cited by 75 publications

References 44 publications

An Early Prediction and Classification of Lung Nodule Diagnosis on CT Images Based on Hybrid Deep Learning Techniques

An Early Prediction and Classification of Lung Nodule Diagnosis on CT Images Based on Hybrid Deep Learning Techniques

Diagnostic Classification of Lung Cancer Using Deep Transfer Learning Technology and Multi‐Omics Data

Multislice Spiral CT Image Analysis and Meta-Analysis of Inspiratory Muscle Training on Respiratory Muscle Function

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