Breast Cancer Characterization Based on Image Classification of Tissue Sections Visualized under Low Magnification
المؤلفون المشاركون
Kostopoulos, S.
Sfikas, C.
Loukas, C.
Glotsos, D.
Tanoglidi, A.
Cavouras, D.
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2013، العدد 2013 (31 ديسمبر/كانون الأول 2013)، ص ص. 1-7، 7ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2013-08-31
دولة النشر
مصر
عدد الصفحات
7
التخصصات الرئيسية
الملخص EN
Rapid assessment of tissue biopsies is a critical issue in modern histopathology.
For breast cancer diagnosis, the shape of the nuclei and the architectural pattern of the tissue are evaluated under high and low magnifications, respectively.
In this study, we focus on the development of a pattern classification system for the assessment of breast cancer images captured under low magnification (×10).
Sixty-five regions of interest were selected from 60 images of breast cancer tissue sections.
Texture analysis provided 30 textural features per image.
Three different pattern recognition algorithms were employed (kNN, SVM, and PNN) for classifying the images into three malignancy grades: I–III.
The classifiers were validated with leave-one-out (training) and cross-validation (testing) modes.
The average discrimination efficiency of the kNN, SVM, and PNN classifiers in the training mode was close to 97%, 95%, and 97%, respectively, whereas in the test mode, the average classification accuracy achieved was 86%, 85%, and 90%, respectively.
Assessment of breast cancer tissue sections could be applied in complex large-scale images using textural features and pattern classifiers.
The proposed technique provides several benefits, such as speed of analysis and automation, and could potentially replace the laborious task of visual examination.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Loukas, C.& Kostopoulos, S.& Tanoglidi, A.& Glotsos, D.& Sfikas, C.& Cavouras, D.. 2013. Breast Cancer Characterization Based on Image Classification of Tissue Sections Visualized under Low Magnification. Computational and Mathematical Methods in Medicine،Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-501486
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Loukas, C.…[et al.]. Breast Cancer Characterization Based on Image Classification of Tissue Sections Visualized under Low Magnification. Computational and Mathematical Methods in Medicine No. 2013 (2013), pp.1-7.
https://search.emarefa.net/detail/BIM-501486
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Loukas, C.& Kostopoulos, S.& Tanoglidi, A.& Glotsos, D.& Sfikas, C.& Cavouras, D.. Breast Cancer Characterization Based on Image Classification of Tissue Sections Visualized under Low Magnification. Computational and Mathematical Methods in Medicine. 2013. Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-501486
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-501486
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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