An automatic glioma grading method based on multi-feature extraction and fusion

Zhan, Tianming; Feng, Piaopiao; Hong, Xiangfei; Lu, Zhenyu; Xiao, Liang; Zhang, Yudong

doi:10.3233/thc-171341

Cited by 16 publications

(4 citation statements)

References 23 publications

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“…This dataset is available at smir.ch/BRATS/start2013 for download. This dataset is used in various research studies [19][20][21][22]40].…”

Section: Results and Evaluationmentioning

confidence: 99%

“…Apart from the morphological features, textural features are also very important when it comes to the classification of Glioma [18][19][20][21][22]. As in MRI sequences, cells of brain offer a very powerful textural property.…”

Section: Proposed Ewbprl Methods For Texture Feature Extractionmentioning

confidence: 99%

“…The sensitivity of this method was 97.37%. In 2017, Zhan et al come up with an automated approach for the grading of Glioma with the help of fusion of intensity, volume and texture features along with KNN classifier to deliver accuracy of 87.59 on the BRATS 2015 dataset [20]. In 2018, Gupta et al [21] proposed a clinical decision support system in order to assist the radiologist.…”

Section: Literature Reviewmentioning

confidence: 99%

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Glioma Segmentation and Classification System Based on Proposed Texture Features Extraction Method and Hybrid Ensemble Learning

Bhatele¹,

Bhadauria²

2020

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This paper presents an efficient and accurate automated system based on the hybrid XGBoost with Random forest (XGBRF) ensemble model in order to classify the Glioma (type of mostly diagnosed brain tumor) into low grade and high grade Glioma. In this approach initially global thresholding is employed on various MRI sequence and their fusion combinations in order to perform the accurate segmentation. Then uses a proposed Enhanced wavelet binary pattern run length matrix method (EWBPRL) for textural features extraction from the region of interest or segmented Glioma tumor region. This proposed feature extraction method is based on the Discrete wavelet transform (DWT), Local Binary pattern (LBP) and Gray level run length Matrix (GLRLM) methods to extract texture features from the segmented region. Some morphological features are also computed from the segmented region along the textural features. Finally both these extracted features are employed in order to train a hybrid XGBoost with Random forest ensemble model for the first time. The proposed automated system apart from accurately detecting and segmenting the tumors region from the fused MRI sequences, also tends to determine the grading of Glioma in terms of severity. The proposed system is evaluated on the large size balance local dataset and as well as on the popular global datasets like BRATS 2013 and BRATS 2015. This approach offers an encouraging accuracy of 99.25% on the local dataset with the fusion of T1C+T2+Flair MRI sequence as compare to 96.75% accuracy, which is achieved utilizing the fusion of T1+T1C+T2+Flair MRI sequence.

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“…This dataset is available at smir.ch/BRATS/start2013 for download. This dataset is used in various research studies [19][20][21][22]40].…”

Section: Results and Evaluationmentioning

confidence: 99%

Section: Proposed Ewbprl Methods For Texture Feature Extractionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Glioma Segmentation and Classification System Based on Proposed Texture Features Extraction Method and Hybrid Ensemble Learning

Bhatele¹,

Bhadauria²

2020

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“…(1) Algorithm based on threshold judgment Acceleration and angular velocity can be used as feature quantities to distinguish fall behavior from daily behavior activity [4][5]. The maximum acceleration and maximum angular velocity produced during the fall are quite different from the corresponding values of daily behavioral activities.…”

Section: Fall Detection Algorithmmentioning

confidence: 99%

Motion Fall Detection System for the Elderly Based on Multi-feature Fusion Robot

2020

IJMC

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With the improvement of people's living standards, countries around the world are facing the serious problem of aging in their own countries. Unexpected events such as falls in the elderly have a serious and inestimable impact on the health of the elderly. The purpose of this paper is to study the elderly fall detection system based on multi-feature fusion robot. This article first introduces a fall detection algorithm. Second, it improves the way of feature fusion. It uses feature weighted kernel functions to achieve the weighted fusion of acceleration and angular velocity features to build a fall recognition model based on support vector machines. Then, it analyzes the key parts of the fall recognition model. To verify the generalization performance of the algorithm. Finally, a fall detection system based on the recognition model constructed in this paper is implemented on the Android phone platform, and the practical application effect of the fall detection algorithm is verified. The experiments show that the feature fusion fall monitoring algorithm constructed in this paper has a sensitivity of 90.67%, a specificity of 92.96%, and an accuracy of 92.14%, which verifies the detection performance of the detection algorithm constructed in this paper. The robustness and stability of the drop detection software based on the Android platform.

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Brain Tumor Classification into High-Grade and Low-Grade Gliomas Using Adaboost

Pandey¹,

Trivedi

Bharadwaj

et al. 2022

Lecture Notes in Networks and Systems

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An automatic glioma grading method based on multi-feature extraction and fusion

Abstract: The proposed method is an effective method for automatically grading gliomas and can be applied to real situations.

Cited by 16 publications

References 23 publications

Glioma Segmentation and Classification System Based on Proposed Texture Features Extraction Method and Hybrid Ensemble Learning

Glioma Segmentation and Classification System Based on Proposed Texture Features Extraction Method and Hybrid Ensemble Learning

Motion Fall Detection System for the Elderly Based on Multi-feature Fusion Robot

Brain Tumor Classification into High-Grade and Low-Grade Gliomas Using Adaboost

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