Computerized Decision Support System for Mass Identification in Breast Using Digital Mammogram: A Study on GA-Based Neuro-Fuzzy Approaches

Abstract

In the present work, authors have developed a treatment planning system implementing genetic based neuro-fuzzy approaches for accurate analysis of shape and margin of tumor masses appearing in breast using digital mammogram. It is obvious that a complicated structure invites the problem of over learning and misclassification. In proposed methodology, genetic algorithm (GA) has been used for searching of effective input feature vectors combined with adaptive neuro-fuzzy model for final classification of different boundaries of tumor masses. The study involves 200 digitized mammograms from MIAS and other databases and has shown 86% correct classification rate.

Reliability and Evaluation of Identification Models Exemplified by a Histological Diagnosis Model

“…The position of the features of group III (Table 1: indicating the individual deviating characteristics) in an extended diagnosis ( Figures 4b, 4d, 5d) can be discussed in terms of fuzzy logic principles. In several experiments with fuzzy logic comparable results have been found [9,13]. Here, probability or uncertainty is the basic aspect causing patterns in the model outcomes that can be explained as membership functions [13].…”

“…The process of identifying the level of treatment with growth hormones of veal calves is a rather specific situation for diagnosing in the broader framework of application of DSS in medicine [8][9][10]. Only one feature matters, all other features will only modify the probability that a diagnosis belongs to the correct class.…”

“…The match table (Table 5) shows the relative resemblance between the targets based on equation (7). Except for the diagonal, the green colour, based on the calculations using equation (9), indicates that every target can be diagnosed uniquely compared to any other target. Hence, the separation capability is 100% (equation 10).…”

“…Matrix with the matches between the targets of the model for diagnosis of illegal hormone use in veal calves. The figure in every cell is calculated according to equation (7), the colour of every cell is based on equation (9).…”

Review of medical image classification using the adaptive neuro-fuzzy inference system

“…Different feature extraction techniques are introduced among these works. One of these methods is GA that is applied in[ 45 46 ] for searching significant input feature vectors. In[ 45 ] the authors have developed a treatment planning system implementing genetic-based neuro-fuzzy approaches for accurate analysis of shape and margin of tumor masses appearing in breast using digital mammogram.…”