A novel breast tumor classification algorithm using neutrosophic score features. (March 2016)

• Record Type: Journal Article
• Authors: Amin, Khalid M.; Shahin, A.I.; Guo, Yanhui
• Is Part Of: Measurement. Volume 81(2016:Mar.)
• Page Start: 210
• Page End: 220
• Publication Date: 2016-03
• Subjects: Breast cancer -- Ultrasound images -- Feature extraction -- Neutrosophic similarity score; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2015.12.013
• Languages: English
• ISSNs: 0263-2241

Abstract

Graphical abstract: Highlights: Both texture and morphologic features are employed to classify tumors in breast ultrasound images. New features are proposed based on the neutrosophic similarity score. Classification accuracy is increased by the combining texture, morphologic and neutrosophic score features. Abstract: A lot of studies confirmed the seriousness of breast cancer as the most tumors lethal to women worldwide. Early detection and diagnosis of breast cancer are of great importance to increase treatment options and patients' survival rate. Ultrasound is one of the most frequently used methods to detect and diagnosis breast tumor due to its harmlessness and inexpensiveness. However, problems were found in the tumor diagnosis and classification as benign and malign on ultrasound image for its vagueness, such as speckle noise and low contrast. In this paper, we propose a novel breast tumor classification algorithm that combines texture and morphologic features based on neutrosophic similarity score. Then, a supervised feature selection technique is employed to reduce feature space. Finally, a support vector machine (SVM) classifier is employed to prove the discrimination power of the proposed features set. The proposed system is validated by 112 cases (58 malign, 54 benign). The experimental results show that such features set is promising and 99.1% classification accuracy is achieved.