MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

Kang, Jaeyong; Ullah, Zahid; Gwak, Jeonghwan

doi:10.3390/s21062222

Cited by 324 publications

(178 citation statements)

References 67 publications

(54 reference statements)

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“…A leaf node of tree represents the record set corresponding to the leaf node of decision tree under certain conditions. The decision tree is generated by repeatedly establishing lower-level nodes in branch subsets [12]. CART is a typical binary decision tree, which can do both classification and regression.…”

Section: Mineral Resource Prediction By Amentioning

confidence: 99%

[Retracted] Geological Mineral Energy and Classification Based on Machine Learning

2021

Wireless Communications and Mobile Computing

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In order to mine geological mineral energy and study on geological mineral energy classification, a method based on a wireless sensor was proposed. Of logistic regression, artificial neural networks, random forests, and main wireless sensor algorithms of support vector machine (SVM) with the model in the application of the energy mineral resource prediction practice effects are reviewed and discuss the practical application in the process of sample selection, the wrong points existing in the cost, the uncertainty evaluation, and performance evaluation of the model using wireless sensor algorithm, random forest of the probability distribution of mineralization in the study area is calculated, and five prospecting potential areas are delineated. The results show that the ratio of ore-bearing unit and non-ore-bearing unit is 1 : 1, and the best random forest training model is obtained. 70% of the training sample set was randomly selected as the training set, and the remaining 30% was used as the test set to construct the random forest model. The training accuracy of the model is 96.7%, and the testing accuracy is 96.5%. Both model training accuracy and model testing accuracy are very high, which proves the accuracy of RF model construction and achieves satisfactory results. In this study, a wireless sensor is successfully applied to 3D mineral energy prediction, which makes a positive exploration for mineral resource prediction and evaluation in the future. Finally, the prediction of mineral resource energy based on a wireless sensor is an important trend of future development.

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Section: Mineral Resource Prediction By Amentioning

confidence: 99%

[Retracted] Geological Mineral Energy and Classification Based on Machine Learning

2021

Wireless Communications and Mobile Computing

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“…However, the training time of the pre-trained network was very high. To tackle this issue, Kang et al [ 26 ] computed the feature of brain images using pre-trained networks and trained the classical classifier. They found that the ensemble features computed using DenseNet-169, ShuffleNet V2, and MnasNet with the SVM had the best testing accuracy of 93.72% for four classes (no tumor, glioma, meningioma, and pituitary).…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model

Alanazi

Ali

Hussain

et al. 2022

Sensors

126

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With the advancement in technology, machine learning can be applied to diagnose the mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for the early diagnosis of brain tumors into their subclasses, such as pituitary, meningioma, and glioma. First, various layers of isolated convolutional-neural-network (CNN) models are built from scratch to check their performances for brain MRI images. Then, the 22-layer, binary-classification (tumor or no tumor) isolated-CNN model is re-utilized to re-adjust the neurons’ weights for classifying brain MRI images into tumor subclasses using the transfer-learning concept. As a result, the developed transfer-learned model has a high accuracy of 95.75% for the MRI images of the same MRI machine. Furthermore, the developed transfer-learned model has also been tested using the brain MRI images of another machine to validate its adaptability, general capability, and reliability for real-time application in the future. The results showed that the proposed model has a high accuracy of 96.89% for an unseen brain MRI dataset. Thus, the proposed deep-learning framework can help doctors and radiologists diagnose brain tumors early.

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“…The different types of features include texture, brightness, contrast, shape, Gabor transforms, gray-level co-occurrence matrix (GLCM), and wavelet-based features [ 115 , 120 ], histogram of local binary patterns (LBP) [ 121 ]. On the other hand, recently, deep features that are obtained from deep neural networks such as CNN have been used as input to SVM classifier to classify brain tumors [ 122 ]. In brain tumor classification, it is customary to fuse several features from different extraction models to improve the discrimination power of the machine learning model [ 123 ].…”

Section: Brain Tumor Classification Methodsmentioning

confidence: 99%

“…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%

“…Even though promising progress has been made in classifying brain tumors into their respective types from an MRI brain scan using shallow supervised machine learning algorithms, there are still challenges in classifying brain tumors from an MRI scan. These challenges are mainly due to the ROI detection, and extracting descriptive information using traditionally handcrafted feature extraction techniques is not efficient [ 122 ]. This inefficiency mainly arises due to the complex structure of brain anatomy and the high-density nature of the brain.…”

Section: Brain Tumor Classification Methodsmentioning

confidence: 99%

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A Survey of Brain Tumor Segmentation and Classification Algorithms

Biratu

Schwenker

Ayano³

et al. 2021

J. Imaging

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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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MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

Cited by 324 publications

References 67 publications

[Retracted] Geological Mineral Energy and Classification Based on Machine Learning

[Retracted] Geological Mineral Energy and Classification Based on Machine Learning

Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model

A Survey of Brain Tumor Segmentation and Classification Algorithms

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