Brain Tumor Detection Approaches: A Review

Rao, G. Sethuram; Vydeki, D.

doi:10.1109/icssit.2018.8748692

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…As a result, Malignant tumors pose a greater risk than benign tumors. By segmenting normal tissues from brain lesions, the disease Alzheimer's, Parkinson's, and other neurological disorders can be diagnosed 53 . The most common glial cell‐based brain tumors are gliomas.…”

Section: Types Of Brain Tumorsmentioning

confidence: 99%

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Systematic survey on generative adversarial networks for brain tumor segmentation and classification

Kaur

Singh

Jindal

2023

Concurrency and Computation

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SummaryBrain cancer is one of the leading diseases of death in the world caused due to unwanted proliferation of cells. If left untreated, this growth can spread into other human body parts. So early detection helps to enhance long‐term survival and reduce the mortality rate. A lot of research has been done in the field of brain tumors. There have been numerous previous studies conducted using traditional approaches. So far, few review studies are available on brain tumor segmentation and classification and moreover, they were quite limited in explaining the connections of Generative Adversarial Networks (GAN) for brain tumor segmentation and classification. This review mainly focuses on the GAN architectures for brain tumor segmentation and classification. Therefore, first, an overview of brain tumors, GAN‐based publications, approaches to brain tumor segmentation and classification, data sets, software platforms, and performance parameters, are presented. Finally, GAN applications and current trends are discussed. This thorough survey will aid researchers and beginners in the future in creating a better decision support system.

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Section: Types Of Brain Tumorsmentioning

confidence: 99%

“…By segmenting normal tissues from brain lesions, the disease Alzheimer's, Parkinson's, and other neurological disorders can be diagnosed. 53 The most common glial cell-based brain tumors are gliomas. Gliomas are classified into two types: low-grade gliomas and high-grade gliomas.…”

Section: Types Of Brain Tumorsmentioning

confidence: 99%

Systematic survey on generative adversarial networks for brain tumor segmentation and classification

Kaur

Singh

Jindal

2023

Concurrency and Computation

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“…I. Threshold Method [9] In this method the pixels of image is compared with a threshold value and then get divided. i.e Pixels value are assigned 1 if its values are greater than threshold value and 0 if its value is less than threshold value.…”

Section: Image Segmentation Techniquesmentioning

confidence: 99%

Automatic Brain Tumor Detection: A Review

Srivastava¹,

Singh²

2022

Artificial Intelligence and Communication Technologies

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Brain tumor is a deadly disease and its detection and diagnosis is a primary concern. Human-assisted manual classification results in inaccurate prediction and identification can result from hence we employ computer-aided technology to aid with diagnosis accuracy. Numerous challenges like its position in the brain, tumor type, abnormality of cells, image segmentation, etc. are needed for its cure. After segmentation of MRI images, on the basis of variation found in tumor tissue characteristics, the tumor is mainly divided into 2 categories i.e. malignant and benign. Numerous reviews on Brain Tumor segmentation is presented to simplify and help the researchers working in this field. This research also accentuates the advantage and drawbacks of earlier proposed classification techniques. Different types of tumors, their stages, and numerous techniques carried out in the detection of brain tumor using neural networks are also discussed. Diverse methods and the methodologies presented in this analysis will be advantageous for scholars researching in this domain.

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Detection of brain tumor size using modified deep learning and multilevel thresholding utilizing modified dragonfly optimization algorithm

Kumar

Kuttiappan

2022

Concurrency and Computation

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Nowadays, brain tumor (BT), which is the abnormal growth of the mass of tissues in the human brain, is the main cause of death among kids and adults. The existing methodologies concentrate only on the BT classification and could not detect the BT size. This work proposes to detect the BT size utilizing Modified Deep Learning (MDNN) and multilevel thresholding (MT) utilizing modified dragonfly optimization (MD‐MT) algorithm. Afterward, the Histogram Clipping (HC)‐based Contrast Limited Adaptive Histogram Equalization (CLAHE) approach is implemented for enhancing the contrast of the inputted image, and certain features are extracted as of those contrast‐enhanced images. Next, the image is classified as (i) tumor image and (ii) nontumor image with the aid of MDNN. The tumor part is segmented as the classified tumor affected image utilizing MD‐MT. Here, the proposed HC‐CLAHE attains a 99.74% Spearman Rank Correlation, which is greater among other methods while the proposed MD‐MT attains a higher accuracy (99.73%). For classification, the proposed MDNN and the existing Artificial Neural Network, K‐Nearest Neighbor, Support Vector Machine, and Naïve Bayes are contrasted grounded on their performance in respect of precision, f‐measure, and recall. The proposed work shows excellent performance during the experimental evaluation.

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Brain Tumor Detection Approaches: A Review

Cited by 6 publications

References 31 publications

Systematic survey on generative adversarial networks for brain tumor segmentation and classification

Systematic survey on generative adversarial networks for brain tumor segmentation and classification

Automatic Brain Tumor Detection: A Review

Detection of brain tumor size using modified deep learning and multilevel thresholding utilizing modified dragonfly optimization algorithm

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