Benchmarking Neural Networks Activation Functions for Cancer Detection

Miguel Angel Quiroz Martinez; Josue Ricardo Borja Vernaza; Daniel Humberto Plua Moran; Maikel Yelandi Leyva Vazquez

doi:10.1007/978-3-030-85540-6_110

Benchmarking Neural Networks Activation Functions for Cancer Detection

Miguel Angel Quiroz Martinez, Josue Ricardo Borja Vernaza, Daniel Humberto Plua Moran, Maikel Yelandi Leyva Vazquez

Grupo de Investigación en Inteligencia Artificial y Reconocimiento Facial (GIIAR)

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

Resumen

The choice of the most suitable activation functions for artificial neural networks significantly affects training time and task performance. Breast cancer detection is currently based on the use of neural networks and their selection is an element that affects performance. In the present work, reference information on activation functions in neural networks was analyzed. Exploratory research, comprehensive reading, stepwise approach, and deduction were applied as a method. It resulted in phases of comparative evaluation inactivation functions, a quantitative and qualitative comparison of activation functions, and a prototype of neural network algorithm with activation function to detect cancer; It was concluded that the final results put as the best option to use ReLU for early detection of cancer.

Idioma original	Inglés
Título de la publicación alojada	Human Interaction, Emerging Technologies and Future Systems V - Proceedings of the 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and the 6th IHIET
Subtítulo de la publicación alojada	Future Systems IHIET-FS 2021
Editores	Tareq Ahram, Redha Taiar
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	867-873
Número de páginas	7
ISBN (versión impresa)	9783030855390
DOI	https://doi.org/10.1007/978-3-030-85540-6_110
Estado	Publicada - 2022
Evento	5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and 6th International Conference on Human Interaction and Emerging Technologies: Future Systems, IHIET-FS 2021 - Virtual, Online Duración: 27 ago. 2021 → 29 ago. 2021

Serie de la publicación

Nombre	Lecture Notes in Networks and Systems
Volumen	319

Conferencia

Conferencia	5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and 6th International Conference on Human Interaction and Emerging Technologies: Future Systems, IHIET-FS 2021
Ciudad	Virtual, Online
Período	27/08/21 → 29/08/21

Nota bibliográfica

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

Acceder al documento

10.1007/978-3-030-85540-6_110

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Citar esto

Martinez, M. A. Q., Vernaza, J. R. B., Moran, D. H. P., & Vazquez, M. Y. L. (2022). Benchmarking Neural Networks Activation Functions for Cancer Detection. En T. Ahram, & R. Taiar (Eds.), Human Interaction, Emerging Technologies and Future Systems V - Proceedings of the 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and the 6th IHIET: Future Systems IHIET-FS 2021 (pp. 867-873). (Lecture Notes in Networks and Systems; Vol. 319). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-85540-6_110

Martinez, Miguel Angel Quiroz ; Vernaza, Josue Ricardo Borja ; Moran, Daniel Humberto Plua et al. / Benchmarking Neural Networks Activation Functions for Cancer Detection. Human Interaction, Emerging Technologies and Future Systems V - Proceedings of the 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and the 6th IHIET: Future Systems IHIET-FS 2021. editor / Tareq Ahram ; Redha Taiar. Springer Science and Business Media Deutschland GmbH, 2022. pp. 867-873 (Lecture Notes in Networks and Systems).

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title = "Benchmarking Neural Networks Activation Functions for Cancer Detection",

abstract = "The choice of the most suitable activation functions for artificial neural networks significantly affects training time and task performance. Breast cancer detection is currently based on the use of neural networks and their selection is an element that affects performance. In the present work, reference information on activation functions in neural networks was analyzed. Exploratory research, comprehensive reading, stepwise approach, and deduction were applied as a method. It resulted in phases of comparative evaluation inactivation functions, a quantitative and qualitative comparison of activation functions, and a prototype of neural network algorithm with activation function to detect cancer; It was concluded that the final results put as the best option to use ReLU for early detection of cancer.",

keywords = "Activation functions, Benchmarking, Cancer detection, Neural networks",

author = "Martinez, {Miguel Angel Quiroz} and Vernaza, {Josue Ricardo Borja} and Moran, {Daniel Humberto Plua} and Vazquez, {Maikel Yelandi Leyva}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and 6th International Conference on Human Interaction and Emerging Technologies: Future Systems, IHIET-FS 2021 ; Conference date: 27-08-2021 Through 29-08-2021",

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Martinez, MAQ, Vernaza, JRB, Moran, DHP & Vazquez, MYL 2022, Benchmarking Neural Networks Activation Functions for Cancer Detection. En T Ahram & R Taiar (eds.), Human Interaction, Emerging Technologies and Future Systems V - Proceedings of the 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and the 6th IHIET: Future Systems IHIET-FS 2021. Lecture Notes in Networks and Systems, vol. 319, Springer Science and Business Media Deutschland GmbH, pp. 867-873, 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and 6th International Conference on Human Interaction and Emerging Technologies: Future Systems, IHIET-FS 2021, Virtual, Online, 27/08/21. https://doi.org/10.1007/978-3-030-85540-6_110

Benchmarking Neural Networks Activation Functions for Cancer Detection. / Martinez, Miguel Angel Quiroz; Vernaza, Josue Ricardo Borja; Moran, Daniel Humberto Plua et al.
Human Interaction, Emerging Technologies and Future Systems V - Proceedings of the 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and the 6th IHIET: Future Systems IHIET-FS 2021. ed. / Tareq Ahram; Redha Taiar. Springer Science and Business Media Deutschland GmbH, 2022. p. 867-873 (Lecture Notes in Networks and Systems; Vol. 319).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

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T1 - Benchmarking Neural Networks Activation Functions for Cancer Detection

AU - Martinez, Miguel Angel Quiroz

AU - Vernaza, Josue Ricardo Borja

AU - Moran, Daniel Humberto Plua

AU - Vazquez, Maikel Yelandi Leyva

PY - 2022

Y1 - 2022

N2 - The choice of the most suitable activation functions for artificial neural networks significantly affects training time and task performance. Breast cancer detection is currently based on the use of neural networks and their selection is an element that affects performance. In the present work, reference information on activation functions in neural networks was analyzed. Exploratory research, comprehensive reading, stepwise approach, and deduction were applied as a method. It resulted in phases of comparative evaluation inactivation functions, a quantitative and qualitative comparison of activation functions, and a prototype of neural network algorithm with activation function to detect cancer; It was concluded that the final results put as the best option to use ReLU for early detection of cancer.

AB - The choice of the most suitable activation functions for artificial neural networks significantly affects training time and task performance. Breast cancer detection is currently based on the use of neural networks and their selection is an element that affects performance. In the present work, reference information on activation functions in neural networks was analyzed. Exploratory research, comprehensive reading, stepwise approach, and deduction were applied as a method. It resulted in phases of comparative evaluation inactivation functions, a quantitative and qualitative comparison of activation functions, and a prototype of neural network algorithm with activation function to detect cancer; It was concluded that the final results put as the best option to use ReLU for early detection of cancer.

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KW - Benchmarking

KW - Cancer detection

KW - Neural networks

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Martinez MAQ, Vernaza JRB, Moran DHP, Vazquez MYL. Benchmarking Neural Networks Activation Functions for Cancer Detection. En Ahram T, Taiar R, editores, Human Interaction, Emerging Technologies and Future Systems V - Proceedings of the 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and the 6th IHIET: Future Systems IHIET-FS 2021. Springer Science and Business Media Deutschland GmbH. 2022. p. 867-873. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-85540-6_110