A Study on Genetic Algorithm Based Hybrid Softcomputing Model for Benignancy/Malignancy Detection of Masses Using Digital Mammogram

In present works authors have developed a computerized classification procedure for tumor mass in breasts using digital mammogram. The process implements genetic algorithm and hybrid neuro-fuzzy approaches to classify tumor masses into benign and malignant group in order to assist the physicians for treatment planning. The classification process is based on accurate analysis of shape and margin of tumor mass appearing in breast. The shape features using Fourier descriptors introduce a large number of feature vectors. Thus, to classify different boundaries, a standard multilayer preceptor needs large number of inputs. Simultaneously, to train the network, a large number of training cycles and huge memory are also required. 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 the searching of effective input feature vectors. Adaptive neuro-fuzzy model has been used for final classification of different boundaries of tumor masses. The proposed technique is an innovative soft computing approach that removes the limitation of conventional neural networks and indicates a promising direction of adaptation in a changing environment. The classification system utilizes a Euclidean distance function to detect the belongingness of masses in benign and in malignant classes along with degree of benignancy/malignancy. Presently 200 digitized mammograms from MIAS and other databases have been considered for the experiment and which have shown an average of approximately 86% correct classification as compared with clinical data with a highest rate of 88.9%.