Basurto-Hurtado Jesus A, Cruz-Albarran Irving A, Toledano-Ayala Manuel, Ibarra-Manzano Mario Alberto, Morales-Hernandez Luis A, Perez-Ramirez Carlos A
C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico.
Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico.
Cancers (Basel). 2022 Jul 15;14(14):3442. doi: 10.3390/cancers14143442.
Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.
乳腺癌是全球女性主要的死因之一,全球16%的确诊恶性病变是由乳腺癌导致的。从这个意义上说,尽早诊断这些病变至关重要,以便获得最高的生存几率。虽然有几项研究涉及该领域的特定主题,但它们都没有呈现出完整的全貌,即从图像生成到图像解读。本文对用于检测乳腺癌的图像生成和处理技术进行了全面的最新综述,介绍并讨论了图像生成和处理的潜在候选方法。新方法应考虑巧妙地整合人工智能概念和分类数据,以产生能够具备预期的准确性、精确性和可靠性的现代替代方法,从而减少错误分类。