WITHDRAWN: A comparative study of different machine learning techniques for brain tumor analysis

Yousef, Rammah; Gupta, Gaurav; Vanipriya, Ch.; Yousef, Nabhan

doi:10.1016/j.matpr.2021.03.303

Cited by 17 publications

(6 citation statements)

References 27 publications

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“…Jayprada et.al proposed Adaboost Binary Classifier F used a heterogeneous group of weak learodel, which was then applied to the task of classifying brain MRI images [5].In their proposal, "A comparative investigation of several machine learning algorithms for brain tumour analysis," Rammah Yousef, Gaurav Gupta, C.H. Vanipriya, and Nabhan Yousef The most recent ML techniques were compared in this study, and the ML strategies with competing results were addressed and examined in terms of accuracy and performance [6].…”

Section: Literature Reviewmentioning

confidence: 99%

Brain Disorders Identification by Machine Learning Classifiers

Venkataramanaiah

kumar²,

Reddy³

2023

J. Phys.: Conf. Ser.

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Medical imaging like MRI and CT scan images are crucial for accurately diagnosing human brain disease. The traditional method for tumour analysis relies on the radiologist or physician visually inspecting the specimen, which can result in some incorrect classifications when a large number of MRI pictures need to be processed. An automated intelligent classification system is suggested that requires picture categorization in order to reduce human mistake rates. One of the illnesses that kills the majority of individuals worldwide is the brain tumour. If the tumour is accurately anticipated at an early stage, the likelihood that someone would survive can be increased. The human brain is studied using the magnetic resonance imaging (MRI) method to identify illnesses. In this project, Support Vector Machines (SVM)-based classification approaches are suggested and implemented to classify brain images; DWT will extract features from MRI images. The primary goal of this research is to provide a superior result, which is higher accuracy and reduced error rates for SVM-based MRI brain tumour prediction.

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Section: Literature Reviewmentioning

confidence: 99%

Brain Disorders Identification by Machine Learning Classifiers

Venkataramanaiah

kumar²,

Reddy³

2023

J. Phys.: Conf. Ser.

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“…Here this survey checks on the different large-scale training samples that have high labeling medical costs and little supervision which is an issue in segmenting and classifying images [23]. There is another survey on the different types of gliomas and how different machine-learning models are there that have effectively segmented the scans of these lesions [24]. They developed another peritumoral paper that uses an algorithm that segments those tumors with a specified radius and has also been tested on 285 mp MRI scans [25].…”

Section: Related Workmentioning

confidence: 99%

Custom administering attention module for segmentation of magnetic resonance imaging of the brain

Sangolgi,

Sasikala

2023

IJRES

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<span>Taking into account how brain tumors and gliomas are notorious forms of cancer, the medical field has found several methods to diagnose these diseases, with many algorithms that can segment out the cancer cells in the magnetic resonance imaging (MRI) scans of the brain. This paper has proposed a similar segmenting algorithm called a custom administering attention module. This solution uses a custom U-Net model along with a custom administering attention module that uses an attention mechanism to classify and segment the glioma cells using long-range dependency of the feature maps. The customizations lead to a reduction in code complexity and memory cost. The final model has been tested on the BraTS 2019 dataset and has been compared with other state-of-the-art methods for displaying how much better the proposed model has performed in the category of enhancing, non-enhancing and peritumoral gliomas.</span>

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“…In literature [14], the ML approaches that exist in research data were discussed and listed for improving the performance and overcoming the limitation of medicinal image analytics. An important purpose of this review is to grasp the overview of the demonstrated approaches that offer optimum BT dissect.…”

Section: Introductionmentioning

confidence: 99%