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. 2022 Aug 18:2022:2980691.
doi: 10.1155/2022/2980691. eCollection 2022.

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

R Kalpana  1 , M Anto Bennet  1 , Abdul Wahab Rahmani  2
Affiliations

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

R Kalpana et al. Biomed Res Int. .

Retraction in

Abstract

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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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
Epidemiology of LGG and HGG.
Figure 2
Figure 2
Architecture for suggested model.
Figure 3
Figure 3
A selection of pictures from the Multimodal Brain Dataset (BraTS) [17].
Figure 4
Figure 4
The formation of charges in the cloud [9].
Figure 5
Figure 5
Analysis of specificity, sensitivity, and accuracy using the BraTS 2016 database. (a) Specificity, (b) sensitivity, and (c) accuracy.
Figure 6
Figure 6
Using the BraTS 2016 database, analysis by altering K-Fold. (a) Specificity, (b) sensitivity, and (c) accuracy.
Figure 7
Figure 7
Analysis utilising the BraTS 2017 database with different training data. (a) Specificity, (b) sensitivity, and (c) accuracy.
Figure 8
Figure 8
Analysis utilising the BraTS 2017 database and adjusting K-Fold. (a) Specificity, (b) sensitivity, and (c) accuracy.
Figure 9
Figure 9
Training data analysis utilising the BraTS 2018 database. (a) Specificity, (b) sensitivity, and (c) accuracy.
Figure 10
Figure 10
Using the BraTS 2018 database, examine the effects of changing K-Fold. (a) Sensitivity, (b) accuracy, and (c) specificity.
See this image and copyright information in PMC

References

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