![](/images/graphics-bg.png)
Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning
المؤلفون المشاركون
Wu, Panpan
Sun, Xuanchao
Zhao, Ziping
Wang, Haishuai
Pan, Shirui
Schuller, Björn
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-03-30
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
The classification process of lung nodule detection in a traditional computer-aided detection (CAD) system is complex, and the classification result is heavily dependent on the performance of each step in lung nodule detection, causing low classification accuracy and high false positive rate.
In order to alleviate these issues, a lung nodule classification method based on a deep residual network is proposed.
Abandoning traditional image processing methods and taking the 50-layer ResNet network structure as the initial model, the deep residual network is constructed by combining residual learning and migration learning.
The proposed approach is verified by conducting experiments on the lung computed tomography (CT) images from the publicly available LIDC-IDRI database.
An average accuracy of 98.23% and a false positive rate of 1.65% are obtained based on the ten-fold cross-validation method.
Compared with the conventional support vector machine (SVM)-based CAD system, the accuracy of our method improved by 9.96% and the false positive rate decreased by 6.95%, while the accuracy improved by 1.75% and 2.42%, respectively, and the false positive rate decreased by 2.07% and 2.22%, respectively, in contrast to the VGG19 model and InceptionV3 convolutional neural networks.
The experimental results demonstrate the effectiveness of our proposed method in lung nodule classification for CT images.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wu, Panpan& Sun, Xuanchao& Zhao, Ziping& Wang, Haishuai& Pan, Shirui& Schuller, Björn. 2020. Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1138971
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wu, Panpan…[et al.]. Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1138971
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wu, Panpan& Sun, Xuanchao& Zhao, Ziping& Wang, Haishuai& Pan, Shirui& Schuller, Björn. Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1138971
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1138971
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)