RETRACTED ARTICLE: Robust retinal blood vessel segmentation using convolutional neural network and support vector machine

Balasubramanian, Kishore; Ananthamoorthy, N. P.

doi:10.1007/s12652-019-01559-w

Cited by 53 publications

(11 citation statements)

References 30 publications

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“…where d is the dilation factor, k is the filter size, and s−d•i denotes the past direction. Convolutional networks learn data features through convolutional kernels, which have the advantages of parameter sharing and a small number of parameters [42]. Therefore, convolutional structures tend to increase data dimensionality in the intermediate layers in order to learn richer data features.…”

Section: Temporal Convolution Modulementioning

confidence: 99%

Multi-step prediction of chlorophyll concentration based on adaptive graph-temporal convolutional network with series decomposition

Chen,

Zhang,

et al. 2023

Meas. Sci. Technol.

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Chlorophyll concentration can well reflect the nutritional status and algal blooms of water bodies, and is an important indicator for evaluating water quality. The prediction of chlorophyll concentration change trend is of great significance to environmental protection and aquaculture. However, there is a complex and indistinguishable nonlinear relationship between many factors affecting chlorophyll concentration. In order to effectively mine the nonlinear features contained in the data. This paper proposes a time-series decomposition adaptive graph-time convolutional network prediction model. Firstly, the original sequence is decomposed into trend component and periodic component by moving average method. Secondly, based on the graph convolutional neural network, the water quality parameter data is modeled, and a parameter embedding matrix is defined. The idea of matrix decomposition is used to assign weight parameters to each node. The adaptive graph convolution learns the relationship between different water quality parameters, updates the state information of each parameter, and improves the learning ability of the update relationship between nodes. Finally, time dependence is captured by time convolution to achieve multi-step prediction of chlorophyll concentration. The validity of the model is verified by the water quality data of the coastal city Beihai. The results show that the prediction effect of this method is better than other methods. It can be used as a scientific resource for environmental management decision-making.

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Section: Temporal Convolution Modulementioning

confidence: 99%

Multi-step prediction of chlorophyll concentration based on adaptive graph-temporal convolutional network with series decomposition

Chen,

Zhang,

et al. 2023

Meas. Sci. Technol.

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“…CAD uses machine learning [ 4 ] and deep learning methods to diagnose and analyze diseases from large-scale electronic medical data [ 5 , 6 ]. For example, Balasubramanian et al [ 7 ] used a method by combining convolutional neural network and support vector machine to segment retinal blood vessels. CAD can provide valuable reference results for medical personnel, reduce the workload of doctors, and help to reduce the occurrence of misdiagnosis to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

Heart failure classification using deep learning to extract spatiotemporal features from ECG

Zhang,

Yuan-Lu,

Tang

et al. 2024

BMC Med Inform Decis Mak

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Background Heart failure is a syndrome with complex clinical manifestations. Due to increasing population aging, heart failure has become a major medical problem worldwide. In this study, we used the MIMIC-III public database to extract the temporal and spatial characteristics of electrocardiogram (ECG) signals from patients with heart failure. Methods We developed a NYHA functional classification model for heart failure based on a deep learning method. We introduced an integrating attention mechanism based on the CNN-LSTM-SE model, segmenting the ECG signal into 2 to 20 s long segments. Ablation experiments showed that the 12 s ECG signal segments could be used with the proposed deep learning model for superior classification of heart failure. Results The accuracy, positive predictive value, sensitivity, and specificity of the NYHA functional classification method were 99.09, 98.9855, 99.033, and 99.649%, respectively. Conclusions The comprehensive performance of this model exceeds similar methods and can be used to assist in clinical medical diagnoses.

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“…An Enhanced Bayesian Arithmetic Classifier is used for accurate categorization in [ 30 ]. [ 31 ] has developed a novel supervised system to enhance performance using Support Vector Machine (SVM). In [ 32 ] the study reveals that the suggested KSVM outperformed SVM and Hidden Markov Model (HMM) in identifying and categorizing objects that move instantly.…”

Section: Introductionmentioning

confidence: 99%

A Modified Artificial Neural Network (ANN)-Based Time Series Prediction of COVID-19 Cases from Multi-Country Data

Majhi

2023

J. Inst. Eng. India Ser. B

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The deadly Corona virus that first appeared in a seafood market in the Wuhan city of China in December 2019 has been causing global distress by claiming lives and collapsing economies. Given its serious nature, there is an urgent need to understand the virus’s future trajectory. The current study predicts the next day confirmed, death and recovery cases of COVID-19 pandemic for India, Italy, Spain, and the USA by using a modified multilayer neural network (MMLNN) model. The spread of the COVID-19 data is collected from the Kaggle website for the period of 22nd January 2020 to 20th April 2020 (i.e., for 90 days). The predicted figures of the spread of the disease have been estimated and compared with the actual values. Higher precision of the estimates has been observed from the MMLNN model compared to the conventional multilayer neural network (MLANN) model. Specifically, the MMLNN model does faster and more efficient training of the data resulting in less error. The paper forecasts the next day figures (i.e., for 21st April) for all the three cases and does the comparison of the results with the actual values reported. A deviation of 6% is obtained for India, and for the other three countries the deviation is below 3.5%. Given the high accuracy predictive power, the authors recommend that the MMLNN model can be integrated into the health policy of the countries that are struggling with the spread of the virus. Specifically, a decision on health policies such as restrictions on movement can be based on the short-range predictions of the spread of the virus infection.

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RETRACTED ARTICLE: Robust retinal blood vessel segmentation using convolutional neural network and support vector machine

Cited by 53 publications

References 30 publications

Multi-step prediction of chlorophyll concentration based on adaptive graph-temporal convolutional network with series decomposition

Multi-step prediction of chlorophyll concentration based on adaptive graph-temporal convolutional network with series decomposition

Heart failure classification using deep learning to extract spatiotemporal features from ECG

A Modified Artificial Neural Network (ANN)-Based Time Series Prediction of COVID-19 Cases from Multi-Country Data

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