Neuroevolution of Convolutional Neural Networks for Breast Cancer Diagnosis Using Western Blot Strips

Author:

Llaguno-Roque José-Luis1ORCID,Barrientos-Martínez Rocio-Erandi2,Acosta-Mesa Héctor-Gabriel2ORCID,Romo-González Tania1ORCID,Mezura-Montes Efrén2ORCID

Affiliation:

1. Instituto de Investigaciones Biológicas, Universidad Veracruzana, Dr. Luis Castelazo Ayala S/N, Industrial Animas, Xalapa C.P. 91190, Veracruz, Mexico

2. Instituto de Investigaciones en Inteligencia Artificial, Universidad Veracruzana, Campus Sur, Calle Paseo Lote II, Sección Segunda N° 112, Nuevo Xalapa, Xalapa C.P. 91097, Veracruz, Mexico

Abstract

Breast cancer has become a global health problem, ranking first in incidences and fifth in mortality in women around the world. In Mexico, the first cause of death in women is breast cancer. This work uses deep learning techniques to discriminate between healthy and breast cancer patients, based on the banding patterns obtained from the Western Blot strip images of the autoantibody response to antigens of the T47D tumor line. The reaction of antibodies to tumor antigens occurs early in the process of tumorigenesis, years before clinical symptoms. One of the main challenges in deep learning is the design of the architecture of the convolutional neural network. Neuroevolution has been used to support this and has produced highly competitive results. It is proposed that neuroevolve convolutional neural networks (CNN) find an optimal architecture to achieve competitive ranking, taking Western Blot images as input. The CNN obtained reached 90.67% accuracy, 90.71% recall, 95.34% specificity, and 90.69% precision in classifying three different classes (healthy, benign breast pathology, and breast cancer).

Publisher

MDPI AG

Subject

Applied Mathematics,Computational Mathematics,General Engineering

Reference31 articles.

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