ICTACT Journal on Soft Computing

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An Ensemble Neuro Fuzzy Algorithm for Breast Cancer Detection and Classification


Affiliations
1 Department of Artificial Intelligence and Machine Learning, Sasi Institute of Technology and Engineering, India
2 Department of Computer Science and Engineering, Pragati Engineering College, India
3 School of Nursing, Johns Hopkins University, United States
4 Department of Information Technology, Excel Engineering College, India
     

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

Keywords

Ensemble, Neuro-Fuzzy Algorithm, Breast Cancer, Classification, Detection.
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  • An Ensemble Neuro Fuzzy Algorithm for Breast Cancer Detection and Classification

Abstract Views: 30  |  PDF Views: 0

Authors

Shaik Mohammad Rafee
Department of Artificial Intelligence and Machine Learning, Sasi Institute of Technology and Engineering, India
Manjula Devarakonda Venkata
Department of Computer Science and Engineering, Pragati Engineering College, India
Kogila Palanimuthu
School of Nursing, Johns Hopkins University, United States
M. Vadivel
Department of Information Technology, Excel Engineering College, India

Abstract


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

Keywords


Ensemble, Neuro-Fuzzy Algorithm, Breast Cancer, Classification, Detection.

References