Is Intensity Inhomogeneity Correction Useful for Classification of Breast Cancer in Sonograms Using Deep Neural Network?
المؤلفون المشاركون
Lee, Chia-Yen
Chen, Guan-Lin
Zhang, Zhong-Xuan
Chou, Yi-Hong
Hsu, Chih-Chung
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-12-04
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
The sonogram is currently an effective cancer screening and diagnosis way due to the convenience and harmlessness in humans.
Traditionally, lesion boundary segmentation is first adopted and then classification is conducted, to reach the judgment of benign or malignant tumor.
In addition, sonograms often contain much speckle noise and intensity inhomogeneity.
This study proposes a novel benign or malignant tumor classification system, which comprises intensity inhomogeneity correction and stacked denoising autoencoder (SDAE), and it is suitable for small-size dataset.
A classifier is established by extracting features in the multilayer training of SDAE; automatic analysis of imaging features by the deep learning algorithm is applied on image classification, thus allowing the system to have high efficiency and robust distinguishing.
In this study, two kinds of dataset (private data and public data) are used for deep learning models training.
For each dataset, two groups of test images are compared: the original images and the images after intensity inhomogeneity correction, respectively.
The results show that when deep learning algorithm is applied on the sonograms after intensity inhomogeneity correction, there is a significant increase of the tumor distinguishing accuracy.
This study demonstrated that it is important to use preprocessing to highlight the image features and further give these features for deep learning models.
In this way, the classification accuracy will be better to just use the original images for deep learning.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Lee, Chia-Yen& Chen, Guan-Lin& Zhang, Zhong-Xuan& Chou, Yi-Hong& Hsu, Chih-Chung. 2018. Is Intensity Inhomogeneity Correction Useful for Classification of Breast Cancer in Sonograms Using Deep Neural Network?. Journal of Healthcare Engineering،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1191229
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Lee, Chia-Yen…[et al.]. Is Intensity Inhomogeneity Correction Useful for Classification of Breast Cancer in Sonograms Using Deep Neural Network?. Journal of Healthcare Engineering No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1191229
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Lee, Chia-Yen& Chen, Guan-Lin& Zhang, Zhong-Xuan& Chou, Yi-Hong& Hsu, Chih-Chung. Is Intensity Inhomogeneity Correction Useful for Classification of Breast Cancer in Sonograms Using Deep Neural Network?. Journal of Healthcare Engineering. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1191229
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1191229
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