Costos de atención del cáncer de mama en el Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado, México

Hernández-Nájera, Olimpia; Cahuana-Hurtado, Lucero; Ávila-Burgos, Leticia

doi:10.21149/12332

Cited by 1 publication

(1 citation statement)

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“…Breast cancer has become a global health problem as it ranks first in the world in terms of incidence and fifth in terms of cancer-related mortality [1]. In Mexico, breast cancer is the first cause of death in women between 30 and 50 years of age, and since 2006, it has replaced cervical cancer as a public health concern, and it is a major challenge for the health system [2].…”

Section: Introductionmentioning

confidence: 99%

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

Llaguno-Roque,

Barrientos-Martínez,

Acosta-Mesa

et al. 2023

MCA

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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).

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Section: Introductionmentioning

confidence: 99%