Shaik, Mohammad Rafee <br/><br/>
Ensemble neuro fuzzy algorithm for breast cancer detection and classification 

 - Vol.14(3), Jan 
 - Chennai  ICT Academy  2024 
 - 3275-3281p. 
<br/><br/> Breast cancer remains a critical global health concern, necessitating<br/>advanced and accurate diagnostic tools. This study introduces an<br/>Ensemble Neuro-Fuzzy Algorithm (ENFA) designed for the detection<br/>and classification of breast cancer. In the background, we address the<br/>limitations of existing methods, emphasizing the need for enhanced<br/>accuracy and interpretability in diagnostic models. The methodology<br/>involves the fusion of neuro-fuzzy systems within an ensemble<br/>framework, leveraging the complementary strengths of both neural<br/>networks and fuzzy logic. The primary contribution lies in the<br/>development of a robust ENFA, which not only improves diagnostic<br/>accuracy but also provides interpretable insights into decision-making<br/>processes. The ensemble nature of the algorithm enhances resilience<br/>and generalization across diverse patient profiles. Experimental results<br/>demonstrate superior performance compared to existing methods,<br/>showcasing heightened sensitivity and specificity in breast cancer<br/>detection. The findings underscore the potential of ENFA as a reliable<br/>tool for early and accurate breast cancer diagnosis. This research<br/>signifies a significant step towards advancing the efficacy of<br/>computational models in medical diagnostics. 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Computer Engineering