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

Abstract

Mammography is the most effective tool for breast mass 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 physicians to detect breast cells abnormalities earlier than other traditional procedures. The main aim of this chapter is to increase physicians' 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 (NBC) classifier. The mammographic mass dataset is used to analyze the proposed method (CSO-NBC). The dataset is preprocessed and divided to train the CSO-NBC system and test it by the 5-fold cross-validation technique. The performance of the proposed classification system is compared with the results from other research to show the efficiency of the system in predicting the severity of breast tumors with the highest accuracy.