Snekha Thakran1, Rakesh Kumar Gupta2, and Anup Singh1,3
1Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India, Delhi, India, 2Department of Radiology, Fortis Memorial Research Institute, Haryana, Gurgaon, India, Delhi, India, 3Department of Biomedical Engineering, All India Institute of Medical Science, New Delhi, India, Delhi, India
1Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India, Delhi, India, 2Department of Radiology, Fortis Memorial Research Institute, Haryana, Gurgaon, India, Delhi, India, 3Department of Biomedical Engineering, All India Institute of Medical Science, New Delhi, India, Delhi, India
The combination of support-vector-machine(SVM) with Wrapper method using Adaptive-Boosting(AdaBoost) technique resulted in high sensitivity(0.94±0.07), specificity(0.80±0.05), and accuracy(0.90±5.48) in classification of low-grade vs. high-grade tumors.
Figure-1: The flow chart of the proposed framework.
Table-1: List of different feature vectors and their description.