Seeded region growing segmentation on ultrasound image using particle swarm optimization

Suman, Parineeta; Parasar, Deepa; Rathod, Vijay R.

doi:10.1109/iccic.2015.7435715

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…(1) Rider Deep LSTM network Rider Deep LSTM operates on time series data (P. Suman , 2015 ). The Rider Deep LSTM's main contribution is the function of memory gates and forget gates, which are used to manage information in order to rebuild appropriate information at all times.…”

Section: Proposed Improved Recurrent Rider Lstmmentioning

confidence: 99%

A Comparative Analysis for Stock Price Prediction Using Improved Expansive Deep LSTM Model

Patil¹,

Parasar²,

Charhate³

2022

INDJCSE

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Due to a lack of clarity and flexibility, prediction leveraging ML models is not well fitted in many sections of commercial decision processes. Proposed model aim to employ deep learning strategy in the stock market pricing area to generate positive risk-adjusted price by analyzing previous transaction data and maintaining greater accuracy with a lower error rate. In this study, the deep learning approach is used, which is capable of handling time-series data. The results are obtained with evaluation of error rate metric MSE & RMSE which express how distant the data points are from the regression line. RMSE measures the dispersion of these residuals. It shows how concentrated the data is on the best fit line. This study compares a unique deep learning methodology with deep LSTM, GA and Harris Hawk optimization. As a part of this analysis results are observed and plotted for the various company stocks dataset, which clearly shows the effectiveness of proposed approach with reduced error rate.

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Section: Proposed Improved Recurrent Rider Lstmmentioning

confidence: 99%

A Comparative Analysis for Stock Price Prediction Using Improved Expansive Deep LSTM Model

Patil¹,

Parasar²,

Charhate³

2022

INDJCSE

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“…For the global best PSO, or gbest PSO (Suman et al, 2015), the neighbourhood for each particle is the entire swarm. The social network adopted by the gbest PSO reflects the star topology.…”

Section: Global Best Psomentioning

confidence: 99%

Particle swarm optimisation K-means clustering segmentation of foetus ultrasound image

Parasar

Rathod²

2017

IJSISE

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The purpose of medical image segmentation is to extract information such as volume, shape, motion of organs for detecting abnormalities from the medical image for improvement and fast diagnosis. In this paper, a segmentation algorithm has been implemented for foetus ultrasound image by particle swarm optimisation (PSO) K-means clustering algorithm with fuzzy filter. Impulsive noise inherent in ultrasound image has been removed using fuzzy filter. Then, PSO K-means clustering segmentation method is applied for partitioning foetus ultrasonic images into multiple segments, which applies an optimal suppression factor for the perfect clustering in the specified data set. Experimental results show that the proposed algorithm outperforms other segmentation algorithms like seeded region growing using PSO, fuzzy C-means and watershed in terms of segmentation accuracy for speckle noise added to foetus ultrasound medical images.

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“…This technique is dependent on fuzzy rules in order to filter and make region based detections in images for instance on edges, homogeneous and noisy regions using various gradient filters and fuzzy membership rules. This method is further tested on various imaging modalities like ultra sound images and leads to efficient results that demonstrates its effectiveness [12]. Mohan et al [13] Has recommended a PSO for modifying the enhancement feature CLAHE (Contrast Limited Adaptive Histogram Enhancement) which is based on LCM.…”

Section: Introductionmentioning

confidence: 98%

Efficient Detection and Classification of Diabetic Foot Ulcer Tissue using PSO Technique

Babu¹,

Sabut²,

Nithya³

2018

IJET

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Assessment of a diabetic wound is very much important to determine the healing status. Foot ulcer is most commonly observed problem of diabetic patients. A diabetic wound is observed for approximately 15 per cent of diabetic patients. Diabetic wound is a major concern of diabetes mellitus. The foot ulcer is the very much harm full problem related to diabetes mellitus. Here particle swarm optimization (PSO) based optimization technique is used for segmentation of diabetic wounds and classifying into three types of tissues i.e. granulation, necrotic and slough. After the segmentation the different textural features are extracted through Gray Level Co-occurrence Matrix (GLCM). All these features were then fed to two different classifiers, Naive bayes and Hoeffding tree for classifying the tissue types. The experimental results showed that the classification accuracy, sensitivity, specificity are 90.90%, 100%, 87.5% by Naive bayes, and 81.81%, 100%, 77.7% by Hoeffding tree respectively. Hence the PSO optimization techniques along with Naive bayes classifier could be used for the effective segmentation of diabetic wound images.

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Seeded region growing segmentation on ultrasound image using particle swarm optimization

Cited by 7 publications

References 9 publications

A Comparative Analysis for Stock Price Prediction Using Improved Expansive Deep LSTM Model

A Comparative Analysis for Stock Price Prediction Using Improved Expansive Deep LSTM Model

Particle swarm optimisation K-means clustering segmentation of foetus ultrasound image

Efficient Detection and Classification of Diabetic Foot Ulcer Tissue using PSO Technique

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