Classification Of Human Brain Arteries Using Artificial Neural Network

Abstract

The use of artificial intelligence (AI) techniques in bioinformatics is gaining popularity. There is a growing recognition that many challenges in bioinformatics require a new method, due to the intractability of present approaches or the absence of an educated and intelligent manner to use biological data. A neural network is a data processing model that is based on the activity of neurons, or nerve cells, present in the human brain and nervous system. It s really in need of an information processing system for use in forecasting that operates in the same manner as the human nervous system called neural network model. newline newlineClassification of arteries from medical images is the first step in many image analysis applications developed for medical diagnosis. Other uses include the creation of treatment programmes and the monitoring of illness development. These applications stem from the fact that diseases affect specific arteries, lead to loss, atrophy (volume loss), and abnormalities. Consequently, an accurate, reliable, and automatic classification of these arteries can improve diagnosis and treatment of diseases. Manual classification, although prone to rater drift and bias, is usually accurate but is impractical for large datasets because it is tedious and time consuming. Automatic classification methods can be useful for clinical applications if they have: newline newline1) ability to classify like an expert; newline newline2) excellent performance for diverse datasets; and newline newline3) reasonable processing speed. newline newlineArtificial Neural Networks (ANNs) have been created for a variety of applications, including function approximation, feature extraction, optimization, and classification. In particular, they have been developed for image enhancement, classification, registration, feature extraction, and object recognition. Among these, image classification is more important as it is a critical step for high-level processing such as object recognition. Image segmentation has been accomplished using Multi-Layer Perceptron (MLP), Radial Basis Function (RBF