Abstract
In recent years the field of computer vision has been one of the most advanced in computer science. This is due to the impact of the new techniques in artificial intelligence being deep learning models as the biggest milestone in this field. Computer vision has many applications, but medical diagnosis is one of the most beneficial. Not only in terms of public health, but also in economic benefits. Many medical tests are images, from well-known X-rays to other less used such as thermographies that are cheaper and less invasive. In this paper, we present a hybrid model combining deep learning models such as convolutional neural networks with a weighted average algorithm. The model is trained with thermography images, and we will benefit from segmenting them into the red, green, and blue channels. Then, the weighted average algorithm will calculate the final diagnosis by combining the three previous models. The aim is not only to obtain an accurate model for breast cancer diagnosis but to know what the influence of the different color channels is. Results show that although by separating colors the red channel obtains better accuracy, when using a weighted average algorithm increases by giving more weight to the green color. In this case, accuracy goes near to 97%.
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Acknowledgments
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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Nogales, A., Pérez-Lara, F., Morales, J., García-Tejedor, Á.J. (2023). How Do Thermography Colors Influence Breast Cancer Diagnosis? A Hybrid Model of Convolutional Networks with a Weighted Average Evolutionary Algorithm. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 543. Springer, Cham. https://doi.org/10.1007/978-3-031-16078-3_17
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