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Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics.
- Source :
- International Journal of Biomedical Imaging; 5/8/2018, p1-11, 11p
- Publication Year :
- 2018
-
Abstract
- Gliomas are the most common primary brain tumors, and the objective grading is of great importance for treatment. This paper presents an automatic computer-aided diagnosis of gliomas that combines automatic segmentation and radiomics, which can improve the diagnostic ability. The MRI data containing 220 high-grade gliomas and 54 low-grade gliomas are used to evaluate our system. A multiscale 3D convolutional neural network is trained to segment whole tumor regions. A wide range of radiomic features including first-order features, shape features, and texture features is extracted. By using support vector machines with recursive feature elimination for feature selection, a CAD system that has an extreme gradient boosting classifier with a 5-fold cross-validation is constructed for the grading of gliomas. Our CAD system is highly effective for the grading of gliomas with an accuracy of 91.27%, a weighted macroprecision of 91.27%, a weighted macrorecall of 91.27%, and a weighted macro-F1 score of 90.64%. This demonstrates that the proposed CAD system can assist radiologists for high accurate grading of gliomas and has the potential for clinical applications. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 16874188
- Database :
- Complementary Index
- Journal :
- International Journal of Biomedical Imaging
- Publication Type :
- Academic Journal
- Accession number :
- 129512487
- Full Text :
- https://doi.org/10.1155/2018/2512037