Severity of Breast Masses Prediction in Mammograms Based on Optimized Naive Bayes Diagnostic System

Abstract

Mammography is the most effective tool for breast masses screening. It is a special CT scan technique used only to detect breast tumors early and accurately. Detecting tumors in its early stage has improved the survival rate for breast cancer patients. Computer aided diagnostic systems help the physicians to detect breast cells abnormalities earlier than other traditional procedures. In this paper, an improved Naive Bayes classifier based on Chicken swarm optimization algorithm (CSO-NBC) is analyzed on mammographic mass dataset. The main aim of this research is to increase physician's ability to determine the severity of a mammographic mass lesion from the BI-RADS features and the patient's age using the bio inspired chicken swarm optimization (CSO) algorithm for naive bayes (NB) classifier. The dataset is preprocessed and divided to train the CSO-NBC system and test it by 5-folds cross validation technique. The performance of our proposed classification system is compared with papers' results of other researchers to show the efficiency of our system in predicting severity of breast tumors with the highest accuracy.