Optimal choice of supervised techniques for MR image classification

Resumen

Magnetic Resonance Imaging (MRI) is a modern, robust method that uses in the detection of various medical problems. In this research work, a trial is used to attempt for the detection of tumour in pancreas MR images. An automated classifier is used for detection of tumour in MR images and avoids the drawbacks of MRI. This automated classifiers can detect automatically, either the MR image is affected or not affected. Features are extracted from MR images using second order statistics approach and are classified by two techniques Support Vector Machine (SVM) and Extreme Learning Machine (ELM). SVM approach has high classification accuracy (96%) which is higher than ELM, while ELM performs faster compared to SVM.

Biografía del autor

Balasubramanian Aruna Devi, Kalasalingam Academy of research and Education, Krishnankoil, (India).

Electronics and Communications engg, Kalasalingam Academy of research and Education, Krishnankoil, (India).

Murugan Pallikonda Rajasekaran, Kalasalingam Academy of research and Education, Krishnankoil, (India).

Professor. Electronics and Communications engg, Kalasalingam Academy of research and Education, Krishnankoil, (India).

Publicado
2020-03-23
Cómo citar
Aruna Devi, B., & Pallikonda Rajasekaran, M. (2020). Optimal choice of supervised techniques for MR image classification. 3C Tecnología. Glosas De Innovación Aplicadas a La Pyme, 313-327. Recuperado a partir de http://ojs.3ciencias.com/index.php/3c-tecnologia/article/view/964