An Effective Methodology for Identification of Bone Related Diseases using Bivariate Gaussian Mixture Model
ShanthiRaju Lanka1, SrinivasYarramalle2
1ShanthiRaju Lanka, Department of Information Technology, GITAM Institute of Technology, GITAM, Visakhapatnam (A.P), India.
2Srinivas Yarramalle, Department of Information Technology, GITAM Institute of Technology, GITAM, Visakhapatnam (A.P), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 448-452 | Volume-8 Issue-5, June 2019 | Retrieval Number: E6962068519/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Medical imaging deals with the analysis of medical data and helps to have more complete study regarding the diseases. Among the various diseases attributed to human anatomy, bone fracture is one such complication which needs to be diagnosed appropriately. This article formulates a methodology for the identification of bone fracture more appropriately such that effective treatment can be imparted. The works are carried out on benchmark dataset and the results showcase accuracy of around 95%.
Keywords: Bone Fracture, Accuracy, Medical Image, Bivariate Gaussian Mixture Model, Recognition Rate.
Scope of the Article: Image Processing