Comparison of spine curvature images classification using support vector machine and K-nearest neighbors.

The spine is one part of the human axial skeleton that serves as the body's primary support. Hence, the health of the spine must be considered. The most common spinal abnormality is scoliosis, with the shape of the spine forming the C and S letters. Along with technology development, spinal abnormalities can be identified using images from X-rays to be processed digitally to help health experts as a second opinion to carry out diagnostics of spinal disorders efficiently and accurately. This research was conducted by designing an image processing system for two spine types, normal and abnormal (i.e., scoliosis), by applying the Gray Level Co-occurrence Matrix (GLCM) feature extraction method and two classification methods: K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). The design of this system aims to determine how effective the method is to classify the spine accuracy. The system accuracy in the KNN method reached 73% at a pixel distance of 100 and a quantization level of 16. For the SVM method, the system accuracy value of 90% was obtained at a pixel distance of 75 and a quantization level of 8. The SVM results achieved better than the KNN. [ABSTRACT FROM AUTHOR]