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Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification
المؤلفون المشاركون
Ribeiro, Eduardo
Uhl, Andreas
Wimmer, Georg
Häfner, Michael
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-10-26
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص EN
Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features.
This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification.
However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases.
In this work we explore Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch (or full training) and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities.
We compare our results with some commonly used features for colonic polyp classification and the good results suggest that features learned by CNNs trained from scratch and the “off-the-shelf” CNNs features can be highly relevant for automated classification of colonic polyps.
Moreover, we also show that the combination of classical features and “off-the-shelf” CNNs features can be a good approach to further improve the results.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Ribeiro, Eduardo& Uhl, Andreas& Wimmer, Georg& Häfner, Michael. 2016. Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1100171
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Ribeiro, Eduardo…[et al.]. Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-16.
https://search.emarefa.net/detail/BIM-1100171
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Ribeiro, Eduardo& Uhl, Andreas& Wimmer, Georg& Häfner, Michael. Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1100171
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1100171
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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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
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