Highly Accurate Grey Neural Network Classifier for an Abdominal Aortic Aneurysm Classification

[摘要] An Abdominal Aorta Aneurysm (AAA) is an abnormal focal dilation of the aorta. Most un-ruptured AAAs areasymptomatic, which leads to the problem of having abdominal malignancy, kidney damage, heart attack and even death. As itis ominous, it requires an astute scrutinizing approach. The significance of this proposed work is to scrutinize the exactlocation of the ruptured region and to make astute report of the pathological condition of AAA by computing the RupturedPotential 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 highquality image, segmentation is carried out using Artificial Neural Network-based segmentation. After segmenting the imageinto samples, 12 features are extracted from the segmented image by Gray Level Co-Occurrence Matrix (GLCM), whichassists 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 toachieve the targeted objective with high accuracy.

[学科分类] 计算机科学（综合）

[关键词] Adaptive median filter;artificial neural network-based segmentation;GLCM;PSO;gray neural network classifier