Accuracy Evaluation of Brain Tumor Detection using Entropy-based Image Thresholding

Alyahya, Amal Q.; Abu-Shareha, Ahmad Adel

doi:10.5815/ijitcs.2018.03.02

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…In this research, the improved signal has been considered based on the generated brain signal in various aspects like human intelligence, memory as well as the capability of better sensations. In the paper [15], the accuracy of the entropy-based thresholding approaches in the detection of brain tumors was studied. A framework for brain tumor segmentation is proposed with the core process of the image thresholding to evaluate the accuracy of the entropies.…”

Section: Related Workmentioning

confidence: 99%

Neural Network Modeling and Correlation Analysis of Brain Plasticity Mechanisms in Stroke Patients

I.V.¹,

Mayorova²,

V.V.³

et al. 2019

IJISA

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The aim of this research is the study of pathogenic signs, prognostically significant for the outcome of the disease and restoration of impaired functions at various stages of recovery after a stroke. This work describes a new method of applying a group of artificial neural network algorithms for each of the criteria and for each period of rehabilitation, and it is aimed at analyzing the structural and functional support of motor and higher cognitive functions, including speech and language as well as brain plasticity after ischemic stroke. The functional magnetic resonance imaging (fMRI, DTI) and clinical data machine learning algorithms were used. Self-organizing Kohonen and probabilistic neural network-based models with different structures and parameters were developed and applied for each criterion for periods of 3, 6, and 12 months of rehabilitation. For correlation analyses and modeling additional classifiers, we used: Decision Tree (DT), Support Vector Machine (SUM), k-Nearest Neighbor (KNN) clustering, and Logistic Regression (LR). In the performance evaluation, sensitivity, specificity, accuracy, error rate, and f-measure were used. The using of clinical parameters and mathematical modeling for analysis of brain plasticity mechanisms in stroke patients allowed in some cases to predict cognitive functions within the accuracy of 85-97%. Moreover, it is shown that the functional systems is represented by various brain structures, its synchronous activity and structural connectivity ensures the rapid and most complete restoration of motor and higher cognitive functions, including speech and language (effective post-stroke plasticity of the brain) after a course of neurorehabilitation.

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Section: Related Workmentioning

confidence: 99%