Neuroevolution of Convolutional Neural Networks for Breast Cancer Diagnosis Using Western Blot Strips
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Mathematical and Computational Applications |
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| Online Access: | https://www.mdpi.com/2297-8747/28/3/72 |
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| author | José-Luis Llaguno-Roque Rocio-Erandi Barrientos-Martínez Héctor-Gabriel Acosta-Mesa Tania Romo-González Efrén Mezura-Montes |
| author_facet | José-Luis Llaguno-Roque Rocio-Erandi Barrientos-Martínez Héctor-Gabriel Acosta-Mesa Tania Romo-González Efrén Mezura-Montes |
| author_sort | José-Luis Llaguno-Roque |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-11T02:11:38Z |
| format | Article |
| id | doaj.art-b661eedd8767402b849062c5e05869cf |
| institution | Directory Open Access Journal |
| issn | 1300-686X 2297-8747 |
| language | English |
| last_indexed | 2024-03-11T02:11:38Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematical and Computational Applications |
| spelling | doaj.art-b661eedd8767402b849062c5e05869cf2023-11-18T11:29:57ZengMDPI AGMathematical and Computational Applications1300-686X2297-87472023-05-012837210.3390/mca28030072Neuroevolution of Convolutional Neural Networks for Breast Cancer Diagnosis Using Western Blot StripsJosé-Luis Llaguno-Roque0Rocio-Erandi Barrientos-Martínez1Héctor-Gabriel Acosta-Mesa2Tania Romo-González3Efrén Mezura-Montes4Instituto de Investigaciones Biológicas, Universidad Veracruzana, Dr. Luis Castelazo Ayala S/N, Industrial Animas, Xalapa C.P. 91190, Veracruz, MexicoInstituto de Investigaciones en Inteligencia Artificial, Universidad Veracruzana, Campus Sur, Calle Paseo Lote II, Sección Segunda N° 112, Nuevo Xalapa, Xalapa C.P. 91097, Veracruz, MexicoInstituto de Investigaciones en Inteligencia Artificial, Universidad Veracruzana, Campus Sur, Calle Paseo Lote II, Sección Segunda N° 112, Nuevo Xalapa, Xalapa C.P. 91097, Veracruz, MexicoInstituto de Investigaciones Biológicas, Universidad Veracruzana, Dr. Luis Castelazo Ayala S/N, Industrial Animas, Xalapa C.P. 91190, Veracruz, MexicoInstituto de Investigaciones en Inteligencia Artificial, Universidad Veracruzana, Campus Sur, Calle Paseo Lote II, Sección Segunda N° 112, Nuevo Xalapa, Xalapa C.P. 91097, Veracruz, MexicoBreast 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).https://www.mdpi.com/2297-8747/28/3/72Western blotbreast cancerneuroevolutionconvolutional neural networks |
| spellingShingle | José-Luis Llaguno-Roque Rocio-Erandi Barrientos-Martínez Héctor-Gabriel Acosta-Mesa Tania Romo-González Efrén Mezura-Montes Neuroevolution of Convolutional Neural Networks for Breast Cancer Diagnosis Using Western Blot Strips Mathematical and Computational Applications Western blot breast cancer neuroevolution convolutional neural networks |
| title | Neuroevolution of Convolutional Neural Networks for Breast Cancer Diagnosis Using Western Blot Strips |
| title_full | Neuroevolution of Convolutional Neural Networks for Breast Cancer Diagnosis Using Western Blot Strips |
| title_fullStr | Neuroevolution of Convolutional Neural Networks for Breast Cancer Diagnosis Using Western Blot Strips |
| title_full_unstemmed | Neuroevolution of Convolutional Neural Networks for Breast Cancer Diagnosis Using Western Blot Strips |
| title_short | Neuroevolution of Convolutional Neural Networks for Breast Cancer Diagnosis Using Western Blot Strips |
| title_sort | neuroevolution of convolutional neural networks for breast cancer diagnosis using western blot strips |
| topic | Western blot breast cancer neuroevolution convolutional neural networks |
| url | https://www.mdpi.com/2297-8747/28/3/72 |
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