<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Computer-aided breast ultrasound (BUS) diagnosis remains a difficult task. One of the challenges is that imbalanced BUS datasets lead to poor performance, especially with regard to low accuracy in the minority (malignant tumor) class. Missed diagnosis of malignant tumors can cause serious consequences, such as delaying treatment and increasing the risk of death. Moreover, many diagnosis methods do not consider classification reliability; thus, some classifications may have a large uncertainty. To resolve such problems, a bounded-abstaining classification model is proposed. It maximizes the area under the ROC curve (AUC) under two abstention constraints. A total of 219 (92 malignant and 127 benign) BUS images are collected from the First Affiliated Hospital of Harbin Medical University, China. The experiment tests BUS datasets of three imbalance levels, and the performance contours are analyzed. The results demonstrate that AUC-rejection curves are less affected by class imbalance than accuracy-rejection curves. Compared with the state-of-the-art, the proposed method yields a significantly larger AUC and G-mean using imbalanced BUS datasets.</p></abstract>

Computer-aided breast ultrasound (BUS) diagnosis remains a difficult task. One of the challenges is that imbalanced BUS datasets lead to poor performance, especially with regard to low accuracy in the minority (malignant tumor) class. Missed diagnosis of malignant tumors can cause serious consequences, such as delaying treatment and increasing the risk of death. Moreover, many diagnosis methods do not consider classification reliability; thus, some classifications may have a large uncertainty. To resolve such problems, a bounded-abstaining classification model is proposed. It maximizes the area under the ROC curve (AUC) under two abstention constraints. A total of 219 (92 malignant and 127 benign) BUS images are collected from the First Affiliated Hospital of Harbin Medical University, China. The experiment tests BUS datasets of three imbalance levels, and the performance contours are analyzed. The results demonstrate that AUC-rejection curves are less affected by class imbalance than accuracy-rejection curves. Compared with the state-of-the-art, the proposed method yields a significantly larger AUC and G-mean using imbalanced BUS datasets.

Bounded–abstaining classification for breast tumors in imbalanced ultrasound images

Shandong Key Laboratory of Computer Networks

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Computer-aided breast ultrasound (BUS) diagnosis remains a difficult task. One of the challenges is that imbalanced BUS datasets lead to poor performance,...

Bounded–abstaining classification for breast tumors in imbalanced ultrasound images

Publicado en línea: 04 jul 2020

Páginas: 325 - 336

Recibido: 30 mar 2019

Aceptado: 20 dic 2019

DOI: https://doi.org/10.34768/amcs-2020-0025

Palabras clave
breast ultrasound (BUS) images, reliable diagnosis, abstaining classification, imbalanced datasets

© 2020 Hongjiao Guan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Bounded–abstaining classification for breast tumors in imbalanced ultrasound images

Publicado en línea: 04 jul 2020

Páginas: 325 - 336

Recibido: 30 mar 2019

Aceptado: 20 dic 2019

DOI: https://doi.org/10.34768/amcs-2020-0025

Palabras clavebreast ultrasound (BUS) images, reliable diagnosis, abstaining classification, imbalanced datasets

© 2020 Hongjiao Guan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Palabras clave
breast ultrasound (BUS) images, reliable diagnosis, abstaining classification, imbalanced datasets