![](/images/graphics-bg.png)
NHL Pathological Image Classification Based on Hierarchical Local Information and GoogLeNet-Based Representations
المؤلفون المشاركون
Li, Siqi
Bai, Jie
Ma, Xiaoqi
Jiang, Huiyan
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-03-21
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Background.
Accurate classification for different non-Hodgkin lymphomas (NHL) is one of the main challenges in clinical pathological diagnosis due to its intrinsic complexity.
Therefore, this paper proposes an effective classification model for three types of NHL pathological images, including mantle cell lymphoma (MCL), follicular lymphoma (FL), and chronic lymphocytic leukemia (CLL).
Methods.
There are three main parts with respect to our model.
First, NHL pathological images stained by hematoxylin and eosin (H&E) are transferred into blue ratio (BR) and Lab spaces, respectively.
Then specific patch-level textural and statistical features are extracted from BR images and color features are obtained from Lab images both using a hierarchical way, yielding a set of hand-crafted representations corresponding to different image spaces.
A random forest classifier is subsequently trained for patch-level classification.
Second, H&E images are cropped and fed into a pretrained google inception net (GoogLeNet) for learning high-level representations and a softmax classifier is used for patch-level classification.
Finally, three image-level classification strategies based on patch-level results are discussed including a novel method for calculating the weighted sum of patch results.
Different classification results are fused at both feature 1 and image levels to obtain a more satisfactory result.
Results.
The proposed model is evaluated on a public IICBU Malignant Lymphoma Dataset and achieves an improved overall accuracy of 0.991 and area under the receiver operating characteristic curve of 0.998.
Conclusion.
The experimentations demonstrate the significantly increased classification performance of the proposed model, indicating that it is a suitable classification approach for NHL pathological images.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Bai, Jie& Jiang, Huiyan& Li, Siqi& Ma, Xiaoqi. 2019. NHL Pathological Image Classification Based on Hierarchical Local Information and GoogLeNet-Based Representations. BioMed Research International،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1123162
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Bai, Jie…[et al.]. NHL Pathological Image Classification Based on Hierarchical Local Information and GoogLeNet-Based Representations. BioMed Research International No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1123162
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Bai, Jie& Jiang, Huiyan& Li, Siqi& Ma, Xiaoqi. NHL Pathological Image Classification Based on Hierarchical Local Information and GoogLeNet-Based Representations. BioMed Research International. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1123162
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1123162
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)