MRI based medical image analysis: Survey on brain tumor grade classification

Mohan, Geethu; Subashini, M. Monica

doi:10.1016/j.bspc.2017.07.007

Cited by 328 publications

(159 citation statements)

References 58 publications

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“…14 The creator hopes to review the present examples in segmentation as well as classification relevant to tumor influenced human brain MR pictures with a goal on gliomas which join astrocytoma. 14 The creator hopes to review the present examples in segmentation as well as classification relevant to tumor influenced human brain MR pictures with a goal on gliomas which join astrocytoma.…”

Section: Literature Reviewmentioning

confidence: 99%

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Optimal feature level fusion based ANFIS classifier for brain MRI image classification

Shankar

Elhoseny

Lakshmanaprabu

et al. 2018

Concurrency and Computation

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The cases identified with Brain tumor have increased with respect to time owing to various reasons. One of the major challenging issues can be defined by incorporating image processing along with data mining models as classification approach. There are various procedures as of now exhibited for segmentation of brain tumor effectively. In any case, it is as yet unequivocal to distinguish the brain tumor from MR images. In this new tumor classifying, considering two significant models, such as Feature Selection (FS) and Machine Learning classification techniques, are extremely valuable for distinguishing and visualizing the tumor in the MRI brain images; it is classified using Adaptive Neuro-Fuzzy Interface System (ANFIS). For better classification of image, Optimal Feature Level Fusion (OFLF) is considered to fuse low and high-level feature of brain image; from this analysis, the images are classifying as Benign or Malignant. From this implementation of medical images, the experiment results are evaluating performance metrics are compared existing classifiers. From the proposed MRI image classification process the accuracy as 96.23%, sensitivity as 92.3%, and specificity as 94.52%, compared to existing classifier. It is in the working platform of MATLAB that this proposed methodology is implemented.

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Section: Literature Reviewmentioning

confidence: 99%

“…14,15 • Neural network displaying requires more prominent computational assets. Since wrong identification prompts lethal outcomes, exactness must be exceedingly high in classification systems like ANN, SVM, and lessening process was considered in PCA.…”

Section: Existing Issues In Brain Mr Imagingmentioning

confidence: 99%

Optimal feature level fusion based ANFIS classifier for brain MRI image classification

Shankar

Elhoseny

Lakshmanaprabu

et al. 2018

Concurrency and Computation

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“…The extent of a tumor's threat depends on a variety of factors such as its type, location, size, life span, and how it spreads and develops. The brain is completely covered by the skull …”

Section: Introductionmentioning

confidence: 99%

“…The brain is completely covered by the skull. [1][2][3] This enables the early detection of brain tumors only if there are a preclinical device and appropriate diagnostic tools to assess intracranial cavity status in the early stages of tumor formation. Even with these tools, it is very difficult to accurately diagnose brain tumors due to their variety in shape, size, and appearance.…”

Section: Introductionmentioning

confidence: 99%

“…34 Kaur et al proposed a feature-based classification method for the diagnosis of cancerous medical images. 2,35 An important characteristic of a feature-based image classification method is its optimal features selection based on pruning the extra features. [36][37][38][39] The main purpose of this paper is to use a new improved version of whale optimization algorithm (WAO) optimization for two purposes: optimal features selection and to optimal weights selection for an MLP ANN system for the final classification.…”

Section: Introductionmentioning

confidence: 99%

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Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

Gong

Gao

Abza

2019

Computational Intelligence

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Accurate and early detection of the brain tumor region has a great impact on the choice of treatment, its success rate, and the follow‐up of the disease process over time. This study presents a new bioinspired technique for the early detection of the brain tumor area to improve the chance of completely healing. The study presents a multistep technique to detect the brain tumor area. Herein, after image preprocessing and image feature extraction, an artificial neural network is used to determine the tumor area in the image. The method is based on using an improved version of the whale optimization algorithm for optimal selection of the features and optimizing the artificial neural network weights for classification. Simulation results of the proposed method are applied to FLAIR, T1, and T2 datasets and are compared with different algorithms. Three performance indexes including correct detection rate, false acceptance rate, and false rejection rate are selected for the system performance analysis. Final results showed the superiority of the proposed method toward the other similar methods.

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