A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain

Roy, Sudipta; Nag, Sanjay; Maitra, Indra Kanta; Bandyopadhyay, Samir Kumar

doi:10.48550/arxiv.1312.6150

Cited by 4 publications

(4 citation statements)

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“…They presented a new method for adapting a general brain atlas to an individual tumour patient image that uses sophisticated models of bio-physio mechanical tumour growth. Roy et al [31] proposed an investigation into automated brain tumour detection and segmentation from brain MRI. Brain tumour segmentation was an important step in extracting information from complex MRI brain images.…”

Section: Literature Surveymentioning

confidence: 99%

Novel Strategies Employing Deep Learning Techniques for Classifying Pathological Brain from MR Images

Khuntia¹,

Sahu²,

Devi³

2022

IJACSA

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Brain tumors are the most widespread as well as disturbing sickness, among a very precise expectancy of life almost in their serious structure. As a consequence, therapy planning is a critical component in enhancing the characteristics of the patient's life. Image modalities like computed tomography (CT), magnetic resonance imaging (MRI), along with ultrasound images are commonly used to assess malignancies in the brain, breast, etc. MRI scans, in evidence, are employed in this study to identify the brain tumors. The application of excellent categorization systems on Magnetic Resonance Imaging (MRI) aids in the accurate detection of brain malignancies. The large quantity of data produced through MRI scan, on the other hand, renders physical distribution of tumor and non-tumor in a given time period impossible. It does, however, come with major obstruction. As a consequence, in order to decrease human mortality, a dependable and automated categorizing approach is necessary. The enormous geological and anatomical heterogeneity of the environment surrounding the brain tumor makes automated classification of brain tumor a difficult undertaking. This paper proposes a classification of Convolutional Neural Networks (CNN) for automated brain tumour diagnosis. To study as well as compare the findings, other convolutional neural network designs such as MobileNet V2, ResNet101, and DenseNet121 are used. Small kernels are employed to carry out the more intricate architectural design. This experiment was carried out using Python and Google Colab. The weight of a neuron is characterized as minute.

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Section: Literature Surveymentioning

confidence: 99%

Novel Strategies Employing Deep Learning Techniques for Classifying Pathological Brain from MR Images

Khuntia¹,

Sahu²,

Devi³

2022

IJACSA

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“…The segmentation of brain tumors in MRI scans is a critical task in medical image segmentation [3]. The objective of brain tumor segmentation is to precisely locate different types of tumor regions within medical images, as demonstrated in Figure 2.…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor MRI Segmentation Method Based on Segment Anything Model

Wei

2024

RIA

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The precise segmentation of different types of brain tumor regions constitutes a critical task in medical image segmentation. Clinically, brain MRI contains abundant information, which can significantly assist doctors in the examination and diagnosis of brain tumor patients. With the advancement of artificial intelligence (AI) and computer technology, some foundational models have increasingly played a pivotal role in the field of computer vision. The Segment Anything Model (SAM) is a fundamental model in the realm of image segmentation, renowned for its exceptional zero-shot segmentation performance and transfer ability, achieving commendable results in natural image processing. To explore the efficacy of SAM in segmenting brain tumor MRI and address the issue of low segmentation accuracy due to uneven image grayscale, a method based on SAM feature fusion is proposed. Features fused from the Transformer and Convolutional Neural Network (CNN) are input into a mask decoder, leveraging the attention mechanism of the Transformer to more effectively capture the global relationships within images, thereby enhancing the precision of the output. Experiments have demonstrated that the method proposed in this study surpasses the segmentation performance of SAM alone, achieving precise segmentation of brain tumor MRI.

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“…[34]. Some applications of Otsu's methodology include structural segmentation in fMRI [35][36][37][38], and noise removal in EEG recordings using wavelet decom-position [39]. In our case, Otsu's methodology was implemented for image segmentation and binarization [40].…”

Section: Introductionmentioning

confidence: 99%

Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold

Covantes-Osuna

López

Paredes

et al. 2021

Sensors

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The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (δ, θ, α and β) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu’s version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.

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A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain

Cited by 4 publications

References 0 publications

Novel Strategies Employing Deep Learning Techniques for Classifying Pathological Brain from MR Images

Novel Strategies Employing Deep Learning Techniques for Classifying Pathological Brain from MR Images

Brain Tumor MRI Segmentation Method Based on Segment Anything Model

Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold

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