Medical image segmentation based on fuzzy 2-partition Kapur entropy using fast recursive algorithm

Virk, Ishpreet Singh; Maini, Raman

doi:10.1504/ijiei.2020.112042

Cited by 4 publications

(3 citation statements)

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“…Meanx =1/n[∑x1 2 +x2 2 + …+xn 2 ] where µx = x 2 -Meanx Mean represent mean of each subtrees populated at depth within each model m1 to m4, then the the standard deviation shows enumeration of numerical results generate that splits into conversion classes as probability {high, medium and low} of MCI to {AD,MCI} to {pMCI} respectively [19]. The probability is assigned to {v1,v2,…vn} vectors, calculated from each bootstrap subsets.…”

Section: End Modulementioning

confidence: 99%

Weighted Randomn Forest Model for Significant Feature and Disease Progression Prediction

Rohini¹,

Surendran²

2022

Preprint

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In recent studies, several machine learning and deep learning prediction models have been proposed for the early detection and classification of various stages of Alzheimer's Disease (AD). Many years before the actual onset of AD, there occur several structural changes in the brain. These structural brain features can be utilized in learning the disease progression from early stage of disease. The various stages of pathology cause mild cognitive impairment (MCI) from their normal cognition and AD from normal cognition. This chapter intends to develop a random forest learning model that utilizes a relevant subset of predictors to diagnose the progression of the disease. The conversion from normal cognition to MCI is identified at an early stage of the onset of structural brain changes. The importance of existing research works lies in more early identification of significant feature that increases the disease progression and appropriate inventions greatly improves subjects' recovery. The ADNI cross-sectional MRI data were analysed in this study that utilized brain curvature, grey matter white matter density, volume of cortical and sub cortical structures, shape of hippocampus, hippocampal subfield volume, Mini-mental state exam (MMSE), Clinical Dementia Rating (CDR), Estimated Total Intracranial Volume, Normalize Whole Brain Volume, and Atlas Scaling Factor for constructing randomized trees and thus predicting the features that cause the progression of disease stages from MCI to Alzheimer’s disease that causes dementia. This implementation model proved to give robust AD conversion probability and identifying significant features that are sufficient for future clinical inferences.

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Section: End Modulementioning

confidence: 99%

Weighted Randomn Forest Model for Significant Feature and Disease Progression Prediction

Rohini¹,

Surendran²

2022

Preprint

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“…K-means is a classical division-based unsupervised clustering algorithm with the advantages of simplicity, efficiency, and ease of implementation, and has been applied in many fields [9]. The algorithm starts with random initialization of a set of cluster centres and then repeats the steps of dividing the data set using the cluster centers and updating the cluster centers within the divided set of clusters until the cluster centers converge [10].…”

Section: Current Status Of Researchmentioning

confidence: 99%

An Adaptive Genetic Algorithm-based Background Elimination Model for English Text

Xiaohui¹

2021

Preprint

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In this paper, an adaptive genetic algorithm is used to conduct an in-depth study and analysis of English text background elimination, and a corresponding model is designed. The curve results after the initial character editorialization are curved and transformed, and the adaptive genetic algorithm is used for the transformation to solve the influence of multiple inflection points of curve images on feature extraction. Then, using the minimum deviation method, the error values of the input characters and the sample set in the spatial coordinate system are calculated, and the deviation values of the angle and the straight line are used to match the characters with the smallest deviation value to match the highest degree. A genetic algorithm is introduced to iterate the feature sets of angles and line segments, and the optimal features are finally derived in the process of cross evolution of generations to improve the recognition accuracy. And the character library is used as input items for average grouping for experiments, and the obtained feature sets are put into the position matrix and compared with the samples in the database one by one. It is found that the improved stroke-structure feature extraction algorithm based on a genetic algorithm can improve the recognition accuracy and better accomplish the recognition task with better results compared to others. Finally, by analyzing the limitations and characteristics of traditional particle swarm optimization algorithm and differential evolution algorithm, and giving full play to the advantages and applicability of different algorithms, a new differential evolution particle swarm algorithm with better performance and more stable performance is proposed. The algorithm is based on the PSO algorithm, and when the population update of the PSO algorithm is stagnant and the search space is limited, the crossover and mutation operations of the DE algorithm are used to perturb the population, increase the diversity of the population, and improve the global optimization ability of the algorithm. The algorithm is tested on a common dataset for text mining to verify the effectiveness and feasibility of the algorithm.

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“…In many cases, the use of the thresholding method needs to find some threshold value to change the pixel color. For this purpose, the analysis of fuzzy 2-partition Kapur entropy can be applied [14].…”

Section: Introductionmentioning

confidence: 99%

Lung X-Ray Image Segmentation Using Heuristic Red Fox Optimization Algorithm

Jaszcz

Połap

Damaševičius

2022

Scientific Programming

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Medical image segmentation identifies an area that should be analyzed later in the processing process, such as for disease recognition and classification. As the image search area is reduced, this action allows for faster computation and analysis. We propose the use of a heuristic red fox heuristic optimization algorithm (RFOA) for medical image segmentation in this paper. The heuristics’ operation was adapted to the analysis of two-dimensional images, with a focus on equation modification and the novel fitness function. The proposed solution analyzes the image by converting the selected pixels to one of two color variants, black or white, based on the threshold value used. Their number is counted, allowing analysis of the chosen threshold. As a result, such analysis results in the automatic selection of the segmentation threshold parameter. Our method propose a new fitness function and the adjustment of RFOA to image analysis. We used a publicly available database of lung X-ray images for evaluation, and based on the results, an accuracy analysis was performed, as well as a discussion of the benefits and drawbacks is presented.

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Medical image segmentation based on fuzzy 2-partition Kapur entropy using fast recursive algorithm

Cited by 4 publications

References 0 publications

Weighted Randomn Forest Model for Significant Feature and Disease Progression Prediction

Weighted Randomn Forest Model for Significant Feature and Disease Progression Prediction

An Adaptive Genetic Algorithm-based Background Elimination Model for English Text

Lung X-Ray Image Segmentation Using Heuristic Red Fox Optimization Algorithm

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