Breast Cancer Detection Using Automated Segmentation and Genetic Algorithms

Autor: María de la Luz Escobar, José I. De la Rosa, Carlos E. Galván-Tejada, Jorge I. Galvan-Tejada, Hamurabi Gamboa-Rosales, Daniel de la Rosa Gomez, Huitzilopoztli Luna-García, José M. Celaya-Padilla
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Diagnostics, Vol 12, Iss 12, p 3099 (2022)
Druh dokumentu: article
ISSN: 12123099
2075-4418
DOI: 10.3390/diagnostics12123099
Popis: Breast cancer is the most common cancer among women worldwide, after lung cancer. However, early detection of breast cancer can help to reduce death rates in breast cancer patients and also prevent cancer from spreading to other parts of the body. This work proposes a new method to design a bio-marker integrating Bayesian predictive models, pyRadiomics System and genetic algorithms to classify the benign and malignant lesions. The method allows one to evaluate two types of images: The radiologist-segmented lesion, and a novel automated breast cancer detection by the analysis of the whole breast. The results demonstrate only a difference of 12% of effectiveness for the cases of calcification between the radiologist generated segmentation and the automatic whole breast analysis, and a 25% of difference between the lesion and the breast for the cases of masses. In addition, our approach was compared against other proposed methods in the literature, providing an AUC = 0.86 for the analysis of images with lesions in breast calcification, and AUC = 0.96 for masses.
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje