Application of Artificial Immune System Approach in MRI Classification

Abstract

research of artificial immune systems (AISs) in recent years. Although AIS has been used in various fields, applying the AIS to medical images is very rare. The purpose of this study is using the clonal selection algorithm (CSA) of artificial immune systems for classifying the brain MRI, and displaying a single organism image which can finally offer faster organism reference information to a doctor; hence reducing the time to ascertain large number of images, so that the doctor can diagnose the nidus more efficiently and accurately. In order to verify the feasibility and efficiency of this method, we adopt statistical theory for manifold assessment and compare with the perceptron network of double layers, FCM method. The result proves that the method of this study is both feasible and useful.

Use of Artificial Immune Systems to Solve Data Processing Problems

“…The method of k-nearest neighbors is an algorithm for automatic classification of objects or regression [3], which can show fairly accurate results.…”

Recent Advances in Artificial Immune Systems: Models and Applications

“…The computation of the stimulation level at closer distance does not [150][151][152] sufficiently reflect its advantage and is too sensitive to the change in the distance. The adoption of pure clone selection and random change operation strategy can impair the convergence of the public and the stability.…”