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Enhancing breast cancer diagnosis with deep learning and evolutionary algorithms: A comparison of approaches using different thermographic imaging treatments

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Abstract 

The medical field has come a long way in recent years. This fact is directly related to the application of computer science, particularly artificial intelligence. Computer vision is one of its applications with the most significant knowledge transfer to private companies or organizations. Due to the large number of tests based on images, it has multiple benefits in medical diagnosis. These benefits go from health to economics, passing through time savings. Most people know X-rays or scanners, but others have not been applied too much like thermographies. Although they are inexpensive, non-invasive, painless, and easy to implement in remote areas, their scientific evidence is not very extended. In this paper, we evaluate different approaches based on four use cases depending on which treatment we applied to the images. This step leads to various scenarios that could benefit from using advanced hybrid Artificial Intelligence models. Evaluating the solutions will not only provide us with an accurate model. Still, it will also allow us to understand further how the different thermograph information influences the diagnosis. Results show that by separating the thermography by three ranges of temperatures and using a hybrid model of convolutional neural networks and evolutive algorithms, we can achieve accuracy near 94%.

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Data is openly available in a public repository that does not issue DOIs.

Notes

  1. http://visual.ic.uff.br/dmi/projeto.php

  2. http://visual.ic.uff.br/dmi/

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Acknowledgements

The work leading to these results has received funding from the “Programa estatal de generación de conocimiento y fortalecimiento científico y tecnológico del sistema de I+D+i”, in the context of the project “Cribado coste-efectivo de cáncer de mama mediante mamografía, ecografía y termografía” (PID2019-110686RB-I00).

We also want to thank Avanade Ibérica for providing a scholarship under the Avanade-UFV Artificial Intelligence Chair agreement.

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  1. CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda Km. 1.800, 28223, Pozuelo de Alarcón, Madrid, Spain

    Alberto Nogales, Fernando Pérez-Lara & Álvaro J. García-Tejedor

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Nogales, A., Pérez-Lara, F. & García-Tejedor, Á.J. Enhancing breast cancer diagnosis with deep learning and evolutionary algorithms: A comparison of approaches using different thermographic imaging treatments. Multimed Tools Appl 83, 42955–42971 (2024). https://doi.org/10.1007/s11042-023-17281-x

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