Highly accurate grey neural network classifier for an abdominal aortic aneurysm classification based on image processing approach

Abstract

An Abdominal Aorta Aneurysm (AAA) is an abnormal focal dilation of the aorta. Most un-ruptured AAAs are asymptomatic, which leads to the problem of having abdominal malignancy, kidney damage, heart attack and even death. As it is ominous, it requires an astute scrutinizing approach. The significance of this proposed work is to scrutinize the exact location of the ruptured region and to make astute report of the pathological condition of AAA by computing the Ruptured Potential Index (RPI). To determine these two factors, image processing is performed in the retrieved image of aneurysm. Initially, it undergoes a process to obtain a high-quality image by making use of Adaptive median filter. After retrieving high quality image, segmentation is carried out using Artificial Neural Network-based segmentation. After segmenting the image into samples, 12 features are extracted from the segmented image by Gray Level Co-Occurrence Matrix (GLCM), which assists in extracting the best feature out of it. This optimization is performed by using Particle Swarm Optimization (PSO). Finally, Grey Neural Network (GNN) classifier is applied to analogize the trained and test set data. This classifier helps to achieve the targeted objective with high accuracy.