Multi‐objective differential evolution‐based ensemble method for brain tumour diagnosis

Rezaei, Kimia; Agahi, Hamed; Mahmoodzadeh, Azar

doi:10.1049/iet-ipr.2018.6377

Cited by 3 publications

(2 citation statements)

References 40 publications

(44 reference statements)

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“…Later, the image edges were enhanced using the unsharp mask filter Jia et al [ 247 ] Segmentation of satellite image GOA-jDE Grasshopper optimization algorithm (GOA) and jDE were combined to form a hybrid GOA-jDE algorithm. The new hybridized algorithm is used to find the optimal threshold value of parameters generated from the image histogram Rezaei et al [ 248 ] Diagnosis of brain tumor from C.T. image Multi-objective DE with ensemble methods At the first image, reconstruction was done to remove noise.…”

Section: Application Of De In Image Processing Problemsmentioning

confidence: 99%

Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review

Chakraborty

Saha

Ezugwu

et al. 2022

Arch Computat Methods Eng

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Differential evolution (DE) is one of the highly acknowledged population-based optimization algorithms due to its simplicity, user-friendliness, resilience, and capacity to solve problems. DE has grown steadily since its beginnings due to its ability to solve various issues in academics and industry. Different mutation techniques and parameter choices influence DE's exploration and exploitation capabilities, motivating academics to continue working on DE. This survey aims to depict DE's recent developments concerning parameter adaptations, parameter settings and mutation strategies, hybridizations, and multi-objective variants in the last twelve years. It also summarizes the problems solved in image processing by DE and its variants.

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Section: Application Of De In Image Processing Problemsmentioning

confidence: 99%

Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review

Chakraborty

Saha

Ezugwu

et al. 2022

Arch Computat Methods Eng

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show abstract

“…This growth rate is very high in infancy and brain development cessation depends on skull development cessation. As this development does not work properly, it can cause problems for the infant, which include neurological disorders [9, 10]. By analysing MRI images obtained from the infant's brain, it is possible to model and extract growth patterns and morphological changes based on these models in order to obtain numerical information on brain development.…”

Section: Related Workmentioning

confidence: 99%

Infant brain segmentation based on a combination of VGG‐16 and U‐Net deep neural networks

et al. 2020

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Medical image segmentation plays a key role in identifying the disease type. In the last decade, various methods have been proposed for medical images segmentation. Despite many efforts made in medical imaging, segmentation of medical images still faces challenges, concerning the variety of shape, location, and texture quality. According to recent studies and magnetic resonance imaging, segmentation of brain images at around 6 months of age is a challenging issue in brain image segmentation due to low tissue contrast between white matter (WM) and grey matter (GM) regions. In this study, using the deep learning model, the convolutional network for the brain fragmentation is presented. First, the image quality is improved using the pre‐processing method. The number of layers utilised in the proposed method is less than that of known models. In the pooling layer, instead of using the maximum function, the averaging function is employed. Sixty‐four batches are also considered to improve the performance of the proposed method. The method is evaluated on the iSeg‐2017 database. The DISC and ASC measures of the proposed method for the three classes of GM, WM, and cerebrovascular fluid are 0.902, 0.594, 0.930, 0.481, 0.971, and 0.231, respectively.

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MRI brain tumor detection using deep learning and machine learning approaches

Anantharajan,

Gunasekaran,

Subramanian

et al. 2024

Measurement: Sensors

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Multi‐objective differential evolution‐based ensemble method for brain tumour diagnosis

Cited by 3 publications

References 40 publications

Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review

Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review

Infant brain segmentation based on a combination of VGG‐16 and U‐Net deep neural networks

MRI brain tumor detection using deep learning and machine learning approaches

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