Optimized Neural Networks for Breast Cancer Classification Using Gene Expression Data

• Ana Beatriz Miranda Valentin UTFPR
• Glaucia Maria Bressan UTFPR
• Leonardo Canuto Junior UTFPR
• Elisângela Ap. da Silva Lizzi UTFPR

DOI: https://doi.org/10.5753/bsb.2024.245194

Abstract

This study aims to develop and evaluate optimized neural networks, including Multilayer Perceptrons (MLP) and Convolutional Neural Networks (CNN), by employing deep learning techniques to classify breast cancer subtypes, based on gene expression data. By implementing different neural network architectures and optimization strategies, this research seeks to determine the accuracy and efficiency of these classification methods. Data is sourced from The Cancer Genome Atlas (TCGA) repository and undergoes preprocessing, including dimensionality reduction, to prepare it for analysis. The contribution is to enhance diagnostic tools, as well as assess the predictive performance of the approaches. The comparison of networks performance presents a promising pathway to enhancing the precision of medical diagnostics and personalize treatment strategies in breast cancer.

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