X-ray image classification based on tumor using GURLS and LIBSVM
A Pooja, R Srinee Mamtha, Vemula Jasmine Sowmya, K. P. Soman · 2016
In today's world, X-ray imaging is the low cost diagnostic technique when compared with all other medical imaging techniques. In this paper, the proposed method is to classify X-ray images based on tumor. The features are extracted using Singular Value Decomposition (SVD) and classified using different kernels in Library for Support Vector Machine (Lib-SVM) and Grand Unified Regularized Least Squares (GURLS). The proposed method is experimented on X-ray image dataset which is approved by an Oncologist. The effectiveness of proposed method is validated based on classification parameters. The experiment result analysis shows that Gaussian-ho in GURLS provides 95% classification accuracy which is 5% higher than RBF kernel in LibSVM. The performance of the proposed system is validated by an Oncologist.