Malignant Breast Cancer Detection Using Feature Selection And Ant Colony Optimization Deep Learning Technique

Authors

  • S.SARANYA, Dr.S.SASIKALA

Abstract

Bosom carcinoma is one among the main tumors for women in developed countries including Asian country. One of the most stunning infirmities is disease carcinoma furthermore; it has a potential reason behind death in women. Consistently, passing rate increments radically because of bosom carcinoma disease. Regardless of the way that disease that cancer is curable in earliest stages the enormous numbers of patients are inspected with carcinoma growth at late stages. There have been a numerous experiential studies tending to Bosom carcinoma machine learning and soft computing techniques. Many case that their algorithms are quicker, simpler, or more precise than others are. The main goal of the research is the classification rule mining algorithm dependent on Ant colony optimization (ACO). The paper focus on the extension of Ant-Miner named CAnt-Miner and CAnt-Miner2 which integrates an entropy-based discretization method in order to managing the non-broken variables in ACO classification algorithms. The main objective of Ant-Miner is to take out the information for classification rules from the selected data. These techniques that are used to identify the carcinoma patients and prognosis depend on the performance of the classifier method was based on predictive accuracy and simplicity of rules

Published

2020-10-17

Issue

Section

Articles