Classificatin of Brain Tumors by Machine Learning Algorithms

Çınarer, Gökalp

doi:10.1109/ismsit.2019.8932878

Cited by 51 publications

(15 citation statements)

References 8 publications

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“…In the literature this dataset was tested with KNN, Random Forest (RF), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) machine learning algorithms. SVM algorithm with 90% accuracy rate was found to be better compared to other algorithms [29]. As seen in the examples, machine learning algorithms are commonly used approaches in classification.…”

Section: Discussionmentioning

confidence: 89%

Brain Tumor Classification Using Deep Neural Network

Çınarer¹,

Arslan²,

Yurttakal³

2020

Adv. sci. technol. eng. syst. j.

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Brain tumors are a type of tumor with a high mortality rate. Since multifocal-looking tumors in the brain can resemble multicentric gliomas or gliomatosis, accurate detection of the tumor is required during the treatment process. The similarity of neurological and radiological findings also complicates the classification of these tumors. Fast and accurate classification is important for brain tumors. Computer aided diagnostic systems and deep neural network architectures can be used in the diagnosis of multicentric gliomas and multiple lesions. In this study, the Deep Neural Network classification model with Synthetic Minority Over-sampling Technique pre-processing was used on the Visually Accessible Rembrandt Images dataset. The proposed model for the classification of brain tumors consists of 1319 trainable parameters and the proposed method has achieved 95.0% accuracy rate. Precision, Recall, F1-measure values are 95.4%, 95.0% and 94.9% respectively. The proposed decision support system can be used to give an idea to doctors in the detection of glioma type tumors.

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Section: Discussionmentioning

confidence: 89%

Brain Tumor Classification Using Deep Neural Network

Çınarer¹,

Arslan²,

Yurttakal³

2020

Adv. sci. technol. eng. syst. j.

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“…Different classification techniques have been proposed by many authors for identifying tumor types from brain images. Different authors have classified tumor into a variety of ways, for instance meningioma, glioma, and pituitary [ 109 , 121 , 122 , 124 , 125 ]; astrocytoma, glioblastoma, and oligodendrogliamo [ 112 ]; glioma tumor grades (I–IV) [ 113 ]; benign and malignant stages(I–IV) [ 126 , 127 , 128 , 129 ]; diffuse midline glioma, medulloblastoma, pilocytic astrocytoma, and ependymoma [ 102 ]; multifocal, multicentric, and gliomatosis [ 130 ]; ependymoma and pilocytic astrocytoma [ 120 ].…”

Section: Brain Tumor Classification Methodsmentioning

confidence: 99%

“…In brain tumor classification, the most commonly used classifiers are neural network [ 108 , 109 , 110 , 111 , 131 ], support vector machines (SVM) [ 108 , 115 , 124 , 127 , 128 , 129 , 130 , 132 , 133 ], K-nearest neighbor (KNN) [ 112 , 121 , 130 , 134 ], Adaboost [ 126 ], and hybrid models [ 113 , 135 , 136 ]. The neural network was implemented using different architectures, such as feedforward neural network [ 110 , 125 ], multilayer perceptron neural network [ 109 , 137 ], and probabilistic neural network (PNN) [ 111 , 131 ].…”

Section: Brain Tumor Classification Methodsmentioning

confidence: 99%

A Survey of Brain Tumor Segmentation and Classification Algorithms

Biratu

Schwenker

Ayano³

et al. 2021

J. Imaging

104

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A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models’ performance evaluation metrics.

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“…The goal of the best machine-learning and classification algorithms was to learn from training automatically and make a wise judgment with high accuracy [180] Liver Disease J48, SVM& NB…”

Section: Genomic Medicine and Machine Learningmentioning

confidence: 99%

Artificial intelligence and machine learning in precision and genomic medicine

Quazi

2022

Med Oncol

152

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The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

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Classificatin of Brain Tumors by Machine Learning Algorithms

Cited by 51 publications

References 8 publications

Brain Tumor Classification Using Deep Neural Network

Brain Tumor Classification Using Deep Neural Network

A Survey of Brain Tumor Segmentation and Classification Algorithms

Artificial intelligence and machine learning in precision and genomic medicine

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