Gratified-built image recovery (GBIR) has twisted into an important and active potentialresearch field with the advance of multimedia and imaging technology. It makes use of image features, such as color, texture and shape, to index images with minimal human intervention.A GBIRsystem can be used to locate medical images in large databases. In this paper we propose a GBIR system which describes the methodology for retrieving digital human brain magnetic resonance images(MRI)based on textural features and the Adaptive Neuro-Ambiguous Inference System (ANAIS) learning to retrieve similar imagesfromdatabase in two categories: normal and tumoral.A fuzzy classifier has been used, because of the uncertainty in the results of classifier and capacity of learning.Adaptive Neuro-Fuzzy Inference System (ANAIS) is a good candidate for our categorization problem. Our proposed GBIR system can locate a query image in the category of normal ortumoralimages in the online Recoverypart. This research uses the knowledge of the GBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. This article and compare the results of the proposed method with the GBIR systemsused in recent works. The experimental results indicate that the proposed method is reliable and has high image recovery Random Walker lgorithm(RWA)efficiency compared with the previous work.