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.
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This work has been supported by the GIIAR research group and the Universidad Politécnica Salesiana.
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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. In: Ahram, T., Taiar, R. (eds) Human Interaction, Emerging Technologies and Future Systems V. IHIET 2021. Lecture Notes in Networks and Systems, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-030-85540-6_110
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DOI: https://doi.org/10.1007/978-3-030-85540-6_110
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