Brain tumor detection based on hybrid deep neural network in MRI by adaptive squirrel search optimization

Deb, Daizy; Roy, Sudipta

doi:10.1007/s11042-020-09810-9

Cited by 51 publications

(12 citation statements)

References 46 publications

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“…So, a lot of work is done for feature extraction in order to forecast the occurrence of a brain tumour. Deb and Roy (2021) [ 12 ] recommended a system to identify picture normalcy and abnormality; we used an adaptive fuzzy deep neural network with frog leap optimization. Classification is done by AFNN, and segmentation is done using adaptive flying squirrel algorithms.…”

Section: Related Workmentioning

confidence: 99%

Metaheuristic Optimization-Driven Novel Deep Learning Approach for Brain Tumor Segmentation

Kalpana

Bennet

Rahmani³

2022

BioMed Research International

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Brain tumor has the foremost distinguished etiology of high morality. Neoplasm, a categorization of brain tumors, is very operative in distinguishing and determining the tumor’s exact location in the brain. Magnetic resonance imaging (MRI) is an efficient noninvasive technique for the anatomical examination of brain tumors. Growth tissues have a distinguishable look in MRI pictures in order that they are unit-wide used for brain tumor feature extraction. The existing research algorithms for brain tumors have some limitations such as different qualities, low sensitivity, and diagnosing the tumor at its stages. In this particular piece of research, an innovative method of optimization known as the procedure for lightning attachment algorithm (PLA) is used, and for the purpose of classification, a CNN model known as DenseNet-169 is applied. PLA was used in order to optimize the growth, and a network model known as the DenseNet-169 model was utilized in order to extract the various growth-optimization choices. First, the MR images of the brain were preprocessed to remove any outliers. Next, the Dense Net-169 CNN model was used to extract network choices from the MR images. In addition, it is used to execute the function of a classifier in order to identify the growth as either an aberrant growth or a traditional growth. In addition, the publicly benchmarked datasets that are widely utilized have validated the algorithmic rule that was granted. The planned system demonstrates the satisfactory accuracy in getting ready to on the dataset and outperforms many of the notable current techniques.

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Section: Related Workmentioning

confidence: 99%

Metaheuristic Optimization-Driven Novel Deep Learning Approach for Brain Tumor Segmentation

Kalpana

Bennet

Rahmani³

2022

BioMed Research International

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“…The suggested hybrid model for low contrast brain tumor MRI images is first trained using a feature dataset after selecting the most important features like; variance, kurtosis, contrast, skewness, and standard deviation collected from online sources are tested. To calculate the classifier score parameters, following mathematical equations are used as given below [17].…”

Section: Confusion Matrixmentioning

confidence: 99%

Brain Tumor Segmentation & Classification using Optimized k-means (SFLA) and Ensemble Learning

2022

pnr

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Brain tumor is a common disease that can occur at any age in humans. Early-stage brain tumor segmentation and classification from lowcontrast MRI images is always difficult. In this paper, a new hybrid optimized k-means algorithm based on the shuffled frog leap algorithm (SFLA) followed by thresholding and morphological with ensemble learning is developed. The proposed work is divided into two segments. After pre-processing of low-contrast MRI images the brain tumor area is calculated from the segmented MRI image then the most efficient features are also extracted using discrete wavelet transformation (DWT) techniques. In the second segment, these extracted features are fed as input parameters into a trained brain tumor classifier using an ensemble learning approach. The ensemble-leaning approach model is trained by a feature dataset collected from an online source. The KNN, decision tree, gradient boosting, random forest, and ANN classifiers are used to classify the type of tumor (benign or malignant) from the low contrast brain tumor MRI image. The proposed framework is more efficient and has an accuracy (average of all models accuracy) of 98.07 percent, sensitivity of 98.21 percent, and specificity of 97.25 percent in predicting the type of brain tumor.

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“…This algorithm is claimed to be effective in the optimization of complicated multidimensional problems with simple principles, handy calculations and reliable results. Ever since its proposal, the squirrel search algorithm has been used in the field of wireless sensor networks [43], brain tumor detection [44], etc. The basic rule of the squirrel search algorithm is as follows.…”

Section: The Basic Principles Of Squirrel Search Algorithmmentioning

confidence: 99%

“…Among them, the SSA with higher solving accuracy and better performance was presented by Jain et al [42] in 2019. Since then, the SSA has been well applied in many practical fields [43,44]. Due to the extensive application of Q-Bézier curves and the significant function that degree reduction plays in data conversion between different CAD/CAM systems, as well as the superiorities and potential possessed by intelligent optimizers in solving optimization problems, this paper propose a new method for the degree reduction of Q-Bézier by incorporating the high-efficient swarm intelligence-based squirrel search algorithm.…”

Section: Introductionmentioning

confidence: 99%

Degree Reduction of Q-Bézier Curves via Squirrel Search Algorithm

Liu

Abbas

et al. 2021

Mathematics

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Q-Bézier curves find extensive applications in shape design owing to their excellent geometric properties and good shape adjustability. In this article, a new method for the multiple-degree reduction of Q-Bézier curves by incorporating the swarm intelligence-based squirrel search algorithm (SSA) is proposed. We formulate the degree reduction as an optimization problem, in which the objective function is defined as the distance between the original curve and the approximate curve. By using the squirrel search algorithm, we search within a reasonable range for the optimal set of control points of the approximate curve to minimize the objective function. As a result, the optimal approximating Q-Bézier curve of lower degree can be found. The feasibility of the method is verified by several examples, which show that the method is easy to implement, and good degree reduction effect can be achieved using it.

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Brain tumor detection based on hybrid deep neural network in MRI by adaptive squirrel search optimization

Cited by 51 publications

References 46 publications

Metaheuristic Optimization-Driven Novel Deep Learning Approach for Brain Tumor Segmentation

Metaheuristic Optimization-Driven Novel Deep Learning Approach for Brain Tumor Segmentation

Brain Tumor Segmentation & Classification using Optimized k-means (SFLA) and Ensemble Learning

Degree Reduction of Q-Bézier Curves via Squirrel Search Algorithm

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