Research on image restoration algorithms based on BP neural network

Xue, Hao; Cui, Hongwei

doi:10.1016/j.jvcir.2019.01.014

Cited by 31 publications

(13 citation statements)

References 25 publications

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“…In forward transmission, the input signal is processed layer by layer from the input layer through the hidden layer to the output layer. In this process, the state of neurons in each layer affects only the state of neurons in the next layer [42] , [43] . If the desired output is not obtained in the output layer, the network is back-propagated and the weights and thresholds are adjusted according to the prediction error, which enables the predicted output of the BPNN to continuously approach the desired output.…”

Section: Methodsmentioning

confidence: 99%

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

Zhang

et al. 2022

Heliyon

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Section: Methodsmentioning

confidence: 99%

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

Zhang

et al. 2022

Heliyon

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“…BP neural network: BP neural network is a multi-layer feed-forward network that train the model according to the error inverse propagation. [22]. DNN: DNN is the deep neural network using convolution operation to extract features of images [12].…”

Section: Baseline Methodsmentioning

confidence: 99%

Lung Nodule Classification Algorithm Based on Fusion Features

2019

Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering

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Lung nodules are the lesion areas of lung, and malignant lung nodules may led to lung cancer. Nowadays, computer-aided diagnosis based on CT images are important for lung cancer. However, the existing methods of feature extraction of CT image did not perform effect. This paper proposes the DSS algorithm for lung nodules classification based on fusion features. The DSS adopts the local-global model to extract the depth features of images, and jointly uses the shape descriptors based on medical knowledge. Besides, it combines the fusion features into the Support Vector Machine (SVM) for lung nodule classification. This paper has evaluated the DSS algorithm on LIDC-IDRI data set and the method performs effect for lung nodules classification.

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“…Here we choose the classic BP algorithm (Rumelhart et al, 1986) as the basic architecture of the neural network. This type of neural network is simple in topology and has a nonlinear mapping ability and thus has been commonly applied in a wide range of fields such as pattern recognition, image processing, and natural language processing (Huang et al, 2005;Chen, 2018;Xue and Cui, 2019;Zhao et al, 2019). Generally, the BP neural network is composed of three categories of layers including input, hidden, and output layers, where users can set multiple hidden layers according to their requirements.…”

Section: Definition Of Factors Related To Cnvsmentioning

confidence: 99%

MFCNV: A New Method to Detect Copy Number Variations From Next-Generation Sequencing Data

Zhao

Huang

et al. 2020

Front. Genet.

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Copy number variation (CNV) is a very important phenomenon in tumor genomes and plays a significant role in tumor genesis. Accurate detection of CNVs has become a routine and necessary procedure for a deep investigation of tumor cells and diagnosis of tumor patients. Next-generation sequencing (NGS) technique has provided a wealth of data for the detection of CNVs at base-pair resolution. However, such task is usually influenced by a number of factors, including GC-content bias, sequencing errors, and correlations among adjacent positions within CNVs. Although many existing methods have dealt with some of these artifacts by designing their own strategies, there is still a lack of comprehensive consideration of all the factors. In this paper, we propose a new method, MFCNV, for an accurate detection of CNVs from NGS data. Compared with existing methods, the characteristics of the proposed method include the following: (1) it makes a full consideration of the intrinsic correlations among adjacent positions in the genome to be analyzed, (2) it calculates read depth, GC-content bias, base quality, and correlation value for each genome bin and combines them as multiple features for the evaluation of genome bins, and (3) it addresses the joint effect among the factors via training a neural network algorithm for the prediction of CNVs. We test the performance of the MFCNV method by using simulation and real sequencing data and make comparisons with several peer methods. The results demonstrate that our method is superior to other methods in terms of sensitivity, precision, and F1-score and can detect many CNVs that other methods have not discovered. MFCNV is expected to be a complementary tool in the analysis of mutations in tumor genomes and can be extended to be applied to the analysis of single-cell sequencing data.

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Research on image restoration algorithms based on BP neural network

Cited by 31 publications

References 25 publications

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

Lung Nodule Classification Algorithm Based on Fusion Features

MFCNV: A New Method to Detect Copy Number Variations From Next-Generation Sequencing Data

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