Bounded-abstaining classification for breast tumors in imbalanced ultrasound images
type: Item
instance of: scholarly article
title: Bounded-abstaining classification for breast tumors in imbalanced ultrasound images
zbMATH Open document ID: 1467.92107
author: Hongjiao Guan
author: H. D. Cheng
author: Xianglong Tang
author: Yingtao Zhang
published in: International Journal of Applied Mathematics and Computer Science
publication date: 2021-05-03T00:00:00Z
review text: The paper is concerned with a classification of breast tumors based on ultrasound images. Given the imbalanced nature of data implying a high uncertainty of results, the proposed bounded-abstaining classifier is investigated, which is aimed at maximizing area under the ROC curve while considering two abstention constraints. Described is an overall design process composed of the following phases: image acquisition, wavelet reduction (to eliminate speckles), feature extraction, feature selection, and classification (realized with the use of kNN and SVM classifiers). The performance of the classifier is assessed with the aid of several performance indicators such as accuracy, area under curve, G-mean, sensitivity, specificity, among others. An effect of imbalanced data on the performance of classification results is investigated. Comparative analysis is carried out leading to the conclusion of the better performance of the developed classification model over its competitors.
reviewed by: Witold Pedrycz
Mathematics Subject Classification ID: 92C55
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 62H30
zbMATH DE Number: 7342354
zbMATH Keywords: ultrasound images
zbMATH Keywords: imbalanced data
zbMATH Keywords: bounded-abstaining classifier
zbMATH Keywords: performance indicators
zbMATH Keywords: diagnosis
MaRDI profile type: Publication
full work available at URL: amcs-2020-0025
OpenAlex ID: W3040734236
cites work: Automated breast cancer detection and classification using ultrasound images: a survey
cites work: A fusion-based approach for breast ultrasound image classification using multiple-ROI texture and morphological analyses
cites work: 10.1162/153244303322753616
cites work: On the use of ROC analysis for the optimization of abstaining classifiers
cites work: Q4536543
DOI: 10.34768/AMCS-2020-0025
description: scientific article; zbMATH DE number 7342354