Arterial Parameters and Elasticity Estimation in Common Carotid Artery Using Deep Learning Approach

Patel, Anoop Kumar; Jain, Sanjay

doi:10.5815/ijigsp.2019.11.03

Cited by 3 publications

(3 citation statements)

References 30 publications

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“…Cineloop capture IMT and LD variations over many cardiac cycles and averaging over multiple frames diminish the effects of noise. Recently, Patel et al [137] estimated elasticity using ELM. ELM was applied for ROI localisation, and in 100 images maximum-IMT and maximum-LD error were 20 mm and 91 mm, respectively.…”

Section: Discussionmentioning

confidence: 99%

Cardiovascular Risk Assessment using Arterial Stiffness: A Deep Learning Framework

N¹,

Shunmugavel

2020

Preprint

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Background: Atherosclerotic cardiovascular disease (CVD) is severe and early-stage detection is crucial. Elevated arterial stiffness observed in childhood atherosclerosis is associated with CVD. Stiffness is an efficient marker of CVD in hypertensives.Assessment of stiffness includes waveform analysis and image-based techniques. Researchers observed several challenges: realtime application, accuracy, operator variability, image quality, scanning procedure, instrument variability and deficiency of standardized procedure in the assessment. Methods: We searched PubMed, Embase and Cochrane online library from inception up to July 2020. Multiple articles on stiffness, pulse wave velocity, assessment and deep learning (DL)-based methods were analysed. Above all, a DL-based technique for assessment of stiffness from cine-loop is proposed. The method includes region of interest (ROI) localisation in multiple frames, segmentation of lumen and parameter estimation. Results: Compared to conventional methods DL provide improved result in lumen diameter and intima-media thickness (IMT) measurements. Using convolutional neural network (CNN), IMT error was 0.08 mm. Further, error using extreme learning machine-autoencoder was 5.79±34.42 \mum. Furthermore, Jaccard index and Dice similarity in fully convolution neural network (FCN) manifested 0.94 and 0.97 for lumen segmentation respectively. Conclusion: This paper focuses on the association of stiffness and atherosclerosis leading to CVD. Success of image-based stiffness estimation depends on the visibility and orientation of arteries, operator experience, intensity variation, shadowing, artefacts, and noise. Traditional methods include transformations to compensate for these challenges. The success of DL-based techniques in segmentation and localisation inspired application in stiffness measurement. DL is used to estimate stiffness from cine-loop.

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Section: Discussionmentioning

confidence: 99%

Cardiovascular Risk Assessment using Arterial Stiffness: A Deep Learning Framework

N¹,

Shunmugavel

2020

Preprint

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“…Output dari fungsi ELM untuk generalized SLFN direpresentasikan oleh persamaan (2). [17], [18], [19].…”

Section: Extreme Learning Machine (Elm)unclassified

Pemodelan Prediksi Harga Ethereum (Atribut: Open, High dan Low) dengan Algoritma Extreme Learning Machine

Kasliono,

Candraningrum,

Sari

2023

bits

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The price of cryptocurrencies such as Ethereum often experiences high fluctuations and is difficult to predict. This study aims to predict Ethereum prices using the Extreme Learning Machine (ELM) algorithm which is a fast and efficient machine learning method. Ethereum price data is collected from CoinMarketCap by scraping the data using CoinmarketCap Scraper from the cryptocmd library using Python. An ELM model is built by changing the number of hidden nodes to determine the optimal prediction model of Ethereum prices based on the smallest average MAPE. Model performance was evaluated using the mean absolute percentage error (MAPE) on the test data set. The results show that the ELM model built can predict Ethereum prices with an accuracy of 96.96%. The MAPE obtained is 3.035334%, with 9 hidden nodes in the ELM network architecture model that was built. This shows that the model can explain about 96.96% of the variation in Ethereum price data. Therefore, the ELM model can be used as an aid in making investment decisions

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“…The hidden layer does not to be neuron alike in this networks [18,19,20]. The output of ELM function for generalized SLFN is represented by the following equation [21,22,23].…”

Section: Extreme Learning Machine (Elm) Algorithmmentioning

confidence: 99%

Point Based Forecasting Model of Vehicle Queue with Extreme Learning Machine Method and Correlation Analysis

Kasliono¹,

Suprapto²,

Makhrus³

2021

IJISA

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Traffic is a medium to move from one point to another. Therefore, the role of traffic is very important to support vehicle mobility. If congestion occurs, mobility will be hampered so that it gives influence to other sectors such as financial, air pollution and traffic violations. This study aims to create a model to predict vehicle queue at the traffic lights when its status is red. The prediction is conducted by using Neural Network with Extreme Learning Machine method to predict the length of the vehicle queue, and Correlation Analysis was used to measure the correlation between the connected roads. The conducted experiments use data of the length of the vehicle queue at the traffic lights which was obtained from DISHUB (Transportation Bureau) DI Yogyakarta. Several experiments were carried out to determine the optimum prediction model of vehicle queue length. The experiments found that the optimum model had an average MAPE value of 15.5882% and an average Running Time of 5.2226 seconds.

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Arterial Parameters and Elasticity Estimation in Common Carotid Artery Using Deep Learning Approach

Cited by 3 publications

References 30 publications

Cardiovascular Risk Assessment using Arterial Stiffness: A Deep Learning Framework

Cardiovascular Risk Assessment using Arterial Stiffness: A Deep Learning Framework

Pemodelan Prediksi Harga Ethereum (Atribut: Open, High dan Low) dengan Algoritma Extreme Learning Machine

Point Based Forecasting Model of Vehicle Queue with Extreme Learning Machine Method and Correlation Analysis

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