Preliminary research on abnormal brain detection by wavelet-energy and quantum- behaved PSO

Zhang, Yudong; Ji, Genlin; Yang, Jiquan; Wang, Shuihua; Dong, Zhengchao; Phillips, P. Jonathon; Sun, Ping

doi:10.3233/thc-161191

Cited by 42 publications

(8 citation statements)

References 36 publications

(33 reference statements)

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“…Secondly wave let energy feature is best for abnormal brain detection. [10]. The disadvantage of this work is, this is very complex work.…”

Section: Various Novel System For Detection Of Brain Tumor Has Usedmentioning

confidence: 96%

“…From methodology point of view classification is important for feature extraction [20]. Determined symmetric or none symmetric features are modelled using preprocessing and post processing techniques for pathological brain detection [6]- [10]. Feature Extraction of Brain MRI is transforming [1], In www.ijacsa.thesai.org DWT, it converts into digital values whereas in SWT can see brain features more accurately.…”

Section: Various Novel System For Detection Of Brain Tumor Has Usedmentioning

confidence: 99%

See 1 more Smart Citation

Segmentation Method for Pathological Brain Tumor and Accurate Detection using MRI

Ejaz¹,

Rahim²,

Rehman³

et al. 2018

ijacsa

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Image segmentation is challenging task in field of medical image processing. Magnetic resonance imaging is helpful to doctor for detection of human brain tumor within three sources of images (axil, corneal, sagittal). MR images are nosier and detection of brain tumor location as feature is more complicated. Level set methods have been applied but due to human interaction they are affected so appropriate contour has been generated in discontinuous regions and pathological human brain tumor portion highlighted after applying binarization, removing unessential objects; therefore contour has been generated. Then to classify tumor for segmentation hybrid Fuzzy K Mean-Self Organization Mapping (FKM-SOM) for variation of intensities is used. For improved segmented accuracy, classification has been performed, mainly features are extracted using Discrete Wavelet Transformation (DWT) then reduced using Principal Component Analysis (PCA). Thirteen features from every image of dataset have been classified for accuracy using Support Vector Machine (SVM) kernel classification (RBF, linear, polygon) so results have been achieved using evaluation parameters like Fscore, Precision, accuracy, specificity and recall.

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“…Secondly wave let energy feature is best for abnormal brain detection. [10]. The disadvantage of this work is, this is very complex work.…”

Section: Various Novel System For Detection Of Brain Tumor Has Usedmentioning

confidence: 96%

Section: Various Novel System For Detection Of Brain Tumor Has Usedmentioning

confidence: 99%

Segmentation Method for Pathological Brain Tumor and Accurate Detection using MRI

Ejaz¹,

Rahim²,

Rehman³

et al. 2018

ijacsa

View full text Add to dashboard Cite

show abstract

“…In (8), σ 2 (t) is the sum of squares of deviations of the particles' fitness values, S stands for the swarm size, f (t) i is the fitness of the i−th particle at the t−th iteration, f (t) avg is the average fitness of the swarm at the t−th iteration, and F is the normalized calibration factor to confine σ 2 (t). Lu et al [20] defined F as (9).…”

Section: Population Diversitymentioning

confidence: 99%

“…Unlike PSO, QPSO needs no velocity vectors for particles, and also has fewer parameters to adjust, making it easier to implement. Since QPSO was proposed, it has attracted much attention and different variants of QPSO have been proposed to enhance the performance from different aspects and successfully applied to solve a wide range of continuous optimization problems [9][10][11][12][13][14]. In general, most current QPSO variants can be classified into three categories [15]: the improvement based on operators from other evolutionary algorithms, hybrid search methods, and cooperative methods.…”

Section: Introductionmentioning

confidence: 99%

Quantum-Behaved Particle Swarm Optimization with Weighted Mean Personal Best Position and Adaptive Local Attractor

Chen¹

2019

Information

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Motivated by concepts in quantum mechanics and particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) was proposed as a variant of PSO with better global search ability. In this paper, a QPSO with weighted mean personal best position and adaptive local attractor (ALA-QPSO) is proposed to simultaneously enhance the search performance of QPSO and acquire good global optimal ability. In ALA-QPSO, the weighted mean personal best position is obtained by distinguishing the difference of the effect of the particles with different fitness, and the adaptive local attractor is calculated using the sum of squares of deviations of the particles’ fitness values as the coefficient of the linear combination of the particle best known position and the entire swarm’s best known position. The proposed ALA-QPSO algorithm is tested on twelve benchmark functions, and compared with the basic Artificial Bee Colony and the other four QPSO variants. Experimental results show that ALA-QPSO performs better than those compared method in all of the benchmark functions in terms of better global search capability and faster convergence rate.

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“…Compared with QSFLA, the search mechanism of QPSO needs fewer parameters [36], and each particle can be directly encoded by real numbers to greatly simplify the evolution process. Meanwhile, particles move in delta potential well of the search space [37]. The congregation of the particle swarm doesn’t lose the randomness and particles can appear on any position of the whole space which is searched in a certain probability.…”

Section: Qsfla-nsmmentioning

confidence: 99%

Underwater sonar image detection: A combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm

Wang

Liu

2017

PLoS ONE

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This paper proposes a combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm to detect underwater objects in sonar images. Specifically, for the first time, the problem of inappropriate filtering degree parameter which commonly occurs in non-local spatial information and seriously affects the denoising performance in sonar images, was solved with the method utilizing a novel filtering degree parameter. Then, a quantum-inspired shuffled frog leaping algorithm based on new search mechanism (QSFLA-NSM) is proposed to precisely and quickly detect sonar images. Each frog individual is directly encoded by real numbers, which can greatly simplify the evolution process of the quantum-inspired shuffled frog leaping algorithm (QSFLA). Meanwhile, a fitness function combining intra-class difference with inter-class difference is adopted to evaluate frog positions more accurately. On this basis, recurring to an analysis of the quantum-behaved particle swarm optimization (QPSO) and the shuffled frog leaping algorithm (SFLA), a new search mechanism is developed to improve the searching ability and detection accuracy. At the same time, the time complexity is further reduced. Finally, the results of comparative experiments using the original sonar images, the UCI data sets and the benchmark functions demonstrate the effectiveness and adaptability of the proposed method.

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Preliminary research on abnormal brain detection by wavelet-energy and quantum- behaved PSO

Cited by 42 publications

References 36 publications

Segmentation Method for Pathological Brain Tumor and Accurate Detection using MRI

Segmentation Method for Pathological Brain Tumor and Accurate Detection using MRI

Quantum-Behaved Particle Swarm Optimization with Weighted Mean Personal Best Position and Adaptive Local Attractor

Underwater sonar image detection: A combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm

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