An Efficient Automatic Mass Classification Method In Digitized Mammograms Using Artificial Neural Network

TL;DR

This paper introduces an efficient ANN-based method for classifying masses in digitized mammograms, focusing on texture features to improve accuracy and reduce false positives in benign-malignant differentiation.

Contribution

The study develops a three-layer ANN utilizing statistical texture features for mass classification, achieving higher sensitivity than radiologists.

Findings

90.91% sensitivity in classification

83.87% specificity achieved

Improves accuracy over radiologists' sensitivity

Abstract

In this paper we present an efficient computer aided mass classification method in digitized mammograms using Artificial Neural Network (ANN), which performs benign-malignant classification on region of interest (ROI) that contains mass. One of the major mammographic characteristics for mass classification is texture. ANN exploits this important factor to classify the mass into benign or malignant. The statistical textural features used in characterizing the masses are mean, standard deviation, entropy, skewness, kurtosis and uniformity. The main aim of the method is to increase the effectiveness and efficiency of the classification process in an objective manner to reduce the numbers of false-positive of malignancies. Three layers artificial neural network (ANN) with seven features was proposed for classifying the marked regions into benign and malignant and 90.91% sensitivity and 83.87% specificity is achieved that is very much promising compare to the radiologist's sensitivity 75%.