Enhancing Breast Cancer Classification: A Deep Learning Approach with Outlier Detection With Egret Swarm Optimization Algorithm and Feature Selection Integration – A Systematic Review

S. Maria  Sylviaa ^1*, N. Sudha ¹

Early Breast Cancer diagnosis via data mining is crucial. Proposed methods achieve high accuracy, improving treatment outcomes for patients significantly.

Abstract

Cancer, the second leading cause of death worldwide, encompasses a spectrum of diseases characterized by uncontrolled cell proliferation. Effective therapy relies on screening, early diagnosis, and recurrence prediction. Timely identification of lesions is critical for improving patient survival rates, and accurate classification of benign cancers can prevent unnecessary treatment. Breast cancer (BC) classification into benign or malignant categories is a subject of extensive review. This review discusses a novel approach using deep neural networks (DNN) and genetic algorithms (GA) based on Egret Swarm Optimization (ESO) to address this challenge. Initially, an Enhanced Linear Discriminant Analysis (ILDA) technique is applied for data dimensionality reduction. Subsequently, DNN controls outlier detection to identify anomalies in the cancer dataset. GA is then employed for feature selection, efficiently locating optimal features in complex search spaces, crucial for cancer diagnosis models, especially non-invasive ones. Finally, ESO algorithm, integrating the aggressive and sit-and-wait tactics of Great Egret and Snowy Egret, respectively, is utilized to classify malignancy. In ths review, The proposed method is evaluated using BC datasets, with trials conducted before and after outlier removal. Experimental results demonstrate the effectiveness and accuracy of the proposed strategy. The findings of this study hold promise for enhancing BC diagnosis and guiding patient treatment decisions.

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

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