Comparison Between Two Novel Approaches in Automatic Breast Cancer Detection and Diagnosis and Its Contribution in Military Defense
2021; Springer Nature; Linguagem: Inglês
10.1007/978-981-16-4884-7_15
ISSN2190-3026
AutoresJackeline Pereira-Carrillo, Diego Suntaxi-Dominguez, Oscar Guarnizo-Cabezas, Fernando Villalba-Meneses, Andrés Tirado-Espín, Diego Almeida-Galárraga,
Tópico(s)Radiomics and Machine Learning in Medical Imaging
ResumoBreast cancerPereira-Carrillo, Jackeline is a serious global healthSuntaxi-Dominguez, Diego problem to whichGuarnizo-Cabezas, Oscar we are all prone, taking into accountVillalba-Meneses, Gandhi the risk factors we areTirado-Espín, Andrés exposedAlmeida-Galárraga, Diego to daily, especially those who work abroad, such as military personnel. An incorrect diagnostic could be translated into a bad or inexistent treatment, and in the worst-case flowing into a patient‘s death. Nowadays, technological approaches allow us to create and design tools to identify and classify these pathologies using Machine learning methods. Nevertheless, the current neural networks are designed to identify and classify natural objects with different properties than medical images have, causing that the predictions made from them do not have medical validity. For those reasons, this paper presents a comparison review between two models of convolutional neural networks, based on modified architectures that pretend to adapt to the unique characteristics of medical images. This work proves the relevance of this technology, its impact into the medical field, and its repercussion and importance of these new tools for the near future of military medicine.
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