![](/images/graphics-bg.png)
A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images
المؤلفون المشاركون
Zhu, Jianqing
Wei, Mengwan
Du, Yongzhao
Wu, Xiuming
Su, Qichen
Zheng, Lixin
Lv, Guorong
Zhuang, Jiafu
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-10-01
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
The classification of benign and malignant based on ultrasound images is of great value because breast cancer is an enormous threat to women’s health worldwide.
Although both texture and morphological features are crucial representations of ultrasound breast tumor images, their straightforward combination brings little effect for improving the classification of benign and malignant since high-dimensional texture features are too aggressive so that drown out the effect of low-dimensional morphological features.
For that, an efficient texture and morphological feature combing method is proposed to improve the classification of benign and malignant.
Firstly, both texture (i.e., local binary patterns (LBP), histogram of oriented gradients (HOG), and gray-level co-occurrence matrixes (GLCM)) and morphological (i.e., shape complexities) features of breast ultrasound images are extracted.
Secondly, a support vector machine (SVM) classifier working on texture features is trained, and a naive Bayes (NB) classifier acting on morphological features is designed, in order to exert the discriminative power of texture features and morphological features, respectively.
Thirdly, the classification scores of the two classifiers (i.e., SVM and NB) are weighted fused to obtain the final classification result.
The low-dimensional nonparameterized NB classifier is effectively control the parameter complexity of the entire classification system combine with the high-dimensional parametric SVM classifier.
Consequently, texture and morphological features are efficiently combined.
Comprehensive experimental analyses are presented, and the proposed method obtains a 91.11% accuracy, a 94.34% sensitivity, and an 86.49% specificity, which outperforms many related benign and malignant breast tumor classification methods.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wei, Mengwan& Du, Yongzhao& Wu, Xiuming& Su, Qichen& Zhu, Jianqing& Zheng, Lixin…[et al.]. 2020. A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1139494
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wei, Mengwan…[et al.]. A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1139494
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wei, Mengwan& Du, Yongzhao& Wu, Xiuming& Su, Qichen& Zhu, Jianqing& Zheng, Lixin…[et al.]. A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1139494
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139494
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
![](/images/ebook-kashef.png)
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
![](/images/kashef-image.png)