Performance Analysis of Search Medical Imaging Service on Cloud Storage Using Decision Trees
Telemedicine services use a large amount of data, most of which are diagnostic images in Digital Imaging and Communications in Medicine (DICOM) and Health Level Seven (HL7) formats. Metadata is generated from each related image to support their identification. This study presents the use of decision trees for the optimization of information search processes for diagnostic images, hosted on the cloud server. To analyze the performance in the server, the following quality of service (QoS) metrics are evaluated: delay, bandwidth, jitter, latency and throughput in five test scenarios for a total of 26 experiments during the loading and downloading of DICOM images, hosted by the telemedicine group server of the Universidad Militar Nueva Granada, Bogotá, Colombia. By applying decision trees as a data mining technique and comparing it with the sequential search, it was possible to evaluate the search times of diagnostic images in the server. The results show that by using the metadata in decision trees, the search times are substantially improved, the computational resources are optimized and the request management of the telemedicine image service is improved. Based on the experiments carried out, search efficiency increased by 45% in relation to the sequential search, given that, when downloading a diagnostic image, false positives are avoided in management and acquisition processes of said information. It is concluded that, for the diagnostic images services in telemedicine, the technique of decision trees guarantees the accessibility and robustness in the acquisition and manipulation of medical images, in improvement of the diagnoses and medical procedures in patients.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2643599Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 334
 Sultan, N. (2014). Making use of cloud computing for healthcare provision: Opportunities and challenges. International Journal of Information Management, 34(2), 177-184.
 Deng, M., Petkovic, M., Nalin, M., & Baroni, I. (2011, July). A Home Healthcare System in the Cloud--Addressing Security and Privacy Challenges. In Cloud Computing (CLOUD), 2011 IEEE International Conference on (pp. 549-556). IEEE.
 Devadass, L., Sekaran, S. S., & Thinakaran, R. (2017). Cloud Computing in Healthcare. International Journal of Students' Research in Technology & Management, 5(1), 25-31.
 Padhy, R. P., Patra, M. R., Ch, S., & Satapathy, R. Design and Implementation of a Cloud based Rural Healthcare Information System Model.
 Doukas, C., & Maglogiannis, I. (2012, July). Bringing IoT and cloud computing towards pervasive healthcare. In Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), 2012 Sixth International Conference on (pp. 922-926). IEEE.
 Pandey, S., Voorsluys, W., Niu, S., Khandoker, A., & Buyya, R. (2012). An autonomic cloud environment for hosting ECG data analysis services. Future Generation Computer Systems, 28(1), 147-154.
 Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98-115.
 Al-Shehri, S. M., Loskot, P., Numanoglu, T., & Mert, M. (2017). Common Metrics for Analyzing, Developing and Managing Telecommunication Networks. arXiv preprint arXiv:1707.03290.
 Amanatullah, Y., Lim, C., Ipung, H. P., & Juliandri, A. (2013, June). Toward cloud computing reference architecture: Cloud service management perspective. In ICT for Smart Society (ICISS), 2013 International Conference on (pp. 1-4). IEEE.
 Ambulkar, B., & Borkar, V. (2012, April). Data mining in cloud computing. In MPGI National Multi Conference (Vol. 2012).
 Raghupathi, W. (2010). Data Mining in Healthcare. Healthcare Informatics: Improving Efficiency and Productivity, 211.
 Kuo, A. M. H. (2011). Opportunities and challenges of cloud computing to improve health care services. Journal of medical Internet research, 13(3).
 Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431-440.
 Guan, Q., Zhang, Z., & Fu, S. (2012). Ensemble of bayesian predictors and decision trees for proactive failure management in cloud computing systems. Journal of Communications, 7(1), 52-61.