Journal of Angiotherapy
Angiogenesis, Inflammation & Therapeutics | Online ISSN  2207-872X
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RESEARCH ARTICLE   (Open Access)
Contents Vol 8 (3)

Enhancing Breast Cancer Classification: A Deep Learning Approach with Outlier Detection with Egret Swarm Optimization Algorithm and Feature Selection Integratio

S. Maria  Sylviaa 1*, N. Sudha 1

+ Author Affiliations

Journal of Angiotherapy 8 (3) 1-13 https://doi.org/10.25163/angiotherapy.839520

Submitted: 07 January 2024 Revised: 29 February 2024  Published: 03 March 2024 

Abstract

Background: Breast cancer (BC) remains a significant global health concern, with high incidence and mortality rates. Early detection and accurate classification are crucial for effective treatment. Traditional BC treatments include surgery, radiation, and medication aimed at eliminating microscopic malignancies. Advances in machine learning (ML) and deep learning (DL) have shown promise in enhancing BC diagnosis and classification accuracy. Method: This study used a novel classification model for BC using a Deep Neural Network-Genetic Algorithm-Evolutionary Strategy Optimization (DNN-GA-ESO) approach. The methodology involves three key phases: data preprocessing using Improved Linear Discriminant Analysis (ILDA), outlier detection via a Deep Neural Network (DNN), and feature selection using a Genetic Algorithm (GA). Finally, the Evolutionary Strategy Optimization (ESO) algorithm classifies the BC data as benign or malignant. The effectiveness of this approach was validated using the Wisconsin Breast Cancer (WBC) and Wisconsin Diagnosis Breast Cancer (WDBC) datasets. Results: The proposed DNN-GA-ESO method demonstrated superior performance in classifying BC. For the WBC dataset, the model achieved an accuracy of 99.30%, precision of 99.35%, F-measure of 0.9936, recall of 99.42%, and kappa statistic of 99.48%. For the WDBC dataset, it achieved an accuracy of 99.45%. These results significantly outperformed existing methods such as K-means with Decision Tree, UPFC with ASVM, and other standard ML algorithms. Conclusion: The DNN-GA-ESO approach enhances BC classification accuracy through efficient outlier detection and feature selection. This method surpasses traditional and current techniques, providing a more reliable and precise diagnostic tool for early BC detection. The integrated meta-algorithm offers a promising solution for medical diagnostics, potentially improving patient outcomes through early and accurate detection of BC. Future work will focus on further refining the model and exploring its applicability to other types of cancer.

Keywords: Breast cancer classification, Deep learning, Genetic algorithms, Outlier detection, Enhanced Swarm Optimization

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