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Fecha
2021-06
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editorial
Elsevier
Citas
Resumen
Background and objective
Breast cancer is the most common cancer in women. While mammography is the most widely used screening technique for the early detection of this disease, it has several disadvantages such as radiation exposure or high economic cost. Recently, multiple authors studied the ability of machine learning algorithms for early diagnosis of breast cancer using thermal images, showing that thermography can be considered as a complementary test to mammography, or even as a primary test under certain circumstances. Moreover, although some personal and clinical data are considered risk factors of breast cancer, none of these works considered that information jointly with thermal images.
Methods
We propose a novel approach for early detection of breast cancer combining thermal images of different views with personal and clinical data, building a multi-input classification model which exploits the benefits of convolutional neural networks for image analysis. First, we searched for structures using only thermal images. Next, we added the clinical data as a new branch of each of these structures, aiming to improve its performance.
Results
We applied our method to the most widely used public database of breast thermal images, the Database for Mastology Research with Infrared Image. The best model achieves a 97% accuracy and an area under the ROC curve of 0.99, with a specificity of 100% and a sensitivity of 83%.
Conclusions
After studying the impact of thermal images and personal and clinical data on multi-input convolutional neural networks for breast cancer diagnosis, we conclude that: (1) adding the lateral views to the front view improves the performance of the classification model, and (2) including personal and clinical data helps the model to recognize sick patients.
Descripción
This is the accepted manuscript of the article. The registered version was first published in Computer Methods and Programs in Biomedicine, 204, is available online at the publisher's website: https://doi.org/10.1016/j.cmpb.2021.106045
Este es el manuscrito aceptado del artículo. La versión registrada fue publicada por primera vez en Computer Methods and Programs in Biomedicine, 204, está disponible en línea en el sitio web del editor: Computer Methods and Programs in Biomedicine, 204
Este es el manuscrito aceptado del artículo. La versión registrada fue publicada por primera vez en Computer Methods and Programs in Biomedicine, 204, está disponible en línea en el sitio web del editor: Computer Methods and Programs in Biomedicine, 204
Categorías UNESCO
Palabras clave
Breast cancer, Classification, Clinical data, Convolutional neural network, Thermal images
Citación
Sánchez-Cauce, R., Pérez-Martín, J., & Luque, M. (2021). Multi-input convolutional neural network for breast cancer detection using thermal images and clinical data. Computer Methods and Programs in Biomedicine, 204. https://doi.org/10.1016/J.CMPB.2021.106045
Centro
Facultad de Ciencias
Departamento
Estadística, Investigación Operativa y Cálculo Numérico