Effective Analysis of Lung Infection using Fuzzy Rules

Abstract

Soft Computing is a consortium of methodologies which works synergistically and provides an ability to make decision from reliable data or expert's experience. Nowadays different types of soft computing techniques such as neural network, fuzzy logic, genetic algorithm and hybrid system are largely used in medical areas. In this paper, algorithm for analysis of lung infection is presented. The main focus is to develop system architecture to find probable disease stage patient may have. Severity level of disease is determined by using rule base method. The algorithm uses output of Rulebase and entered by user to determine level of infection.

A Review on the Rule-Based Filtering Structure with Applications on Computational Biomedical Images

“…Tiwari et al [ 92 ] defined medical entities as fuzzy sets and reasoning as rule-based systems in order to explore the likelihood of incidence of lung infection. It is regarded to be a safe and cost-effective method of treating lung disorders.…”