An Optimize Mechanism for Multifunction Diagnosis of Kidneys by using Genetic Algorithm (International Journal of Engineering and Computer Science, 2014)
In existing system, it was take more time (in minute) to detect and the output was less accurate. The medical technicians laboratory adjust rules and parameters (stored as “templates”) for the included “automatic recognition framework” to achieve results which are closest to those of the clinicians. These parameters can later be used by non experts to achieve increased automation in the identification process. The system’s performance was tested on MRI datasets, while the “automatic 3-D models” created were this research presents a multifunctional platform focusing on the clinical diagnosis of kidneys and their pathology (tumors, stones and cysts), using a “genetic algorithm”. This research presents the automatic tumor detection (ATD) platform: a new system to support a method for increased automation of kidney detection as well as their abnormalities (tumors, stones and cysts). As a first step, specialist clinicians guide the system by accurately annotating validated against the “3-D golden standard models.” Results are promising to give the average accuracy of 97.2% in successfully identifying kidneys and 96.1% of their abnormalities thus outperforming existing methods both in accuracy and in processing time needed. In this paper, the proposed design will define the “genetic algorithm” which will generate the output within a second and more accurate than the existing system.