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High-Performance Method for Brain Tumor Feature Extraction in MRI Using Complex Network
Thanh Han Trong1, Hinh Nguyen Van2, Luu Vu Dang3
1School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, Vietnam.
Objective:
To localize and distinguish between benign and malignant tumors on MRI.
Method:
This work proposes a high-performance method for brain tumor feature extraction using a combination of complex network and U-Net architecture. And then, the common machine-learning algorithms are used to discriminate between benign and malignant tumors. Experiments and Results. The dataset of brain MRI of a total of 230 brain tumor patients in which 77 high-grade glioma patients and 153 low-grade glioma patients were processed. The results of classifying benign and malignant tumors achieved an accuracy of 99.84%.
Conclusion:
The high accuracy of experiment results demonstrates that the use of the complex network and U-Net architecture can significantly improve the accuracy of brain tumor classification. This method could potentially be useful for clinicians in aiding diagnosis and treatment planning for brain tumor patients.
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