دورية أكاديمية
Applications of deep convolutional neural networks to digitized natural history collections
| العنوان: | Applications of deep convolutional neural networks to digitized natural history collections |
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| المؤلفون: | Eric Schuettpelz, Paul Frandsen, Rebecca Dikow, Abel Brown, Sylvia Orli, Melinda Peters, Adam Metallo, Vicki Funk, Laurence Dorr |
| المصدر: | Biodiversity Data Journal, Vol 5, Iss , Pp 1-9 (2017) |
| بيانات النشر: | Pensoft Publishers, 2017. |
| سنة النشر: | 2017 |
| المجموعة: | LCC:Biology (General) |
| مصطلحات موضوعية: | convolutional neural networks, deep learning, Biology (General), QH301-705.5 |
| الوصف: | Natural history collections contain data that are critical for many scientific endeavors. Recent efforts in mass digitization are generating large datasets from these collections that can provide unprecedented insight. Here, we present examples of how deep convolutional neural networks can be applied in analyses of imaged herbarium specimens. We first demonstrate that a convolutional neural network can detect mercury-stained specimens across a collection with 90% accuracy. We then show that such a network can correctly distinguish two morphologically similar plant families 96% of the time. Discarding the most challenging specimen images increases accuracy to 94% and 99%, respectively. These results highlight the importance of mass digitization and deep learning approaches and reveal how they can together deliver powerful new investigative tools. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1314-2828 1314-2836 |
| Relation: | https://bdj.pensoft.net/articles.php?id=21139; https://bdj.pensoft.net/lib/ajax_srv/article_elements_srv.php?action=download_pdf&item_id=21139; https://bdj.pensoft.net/lib/ajax_srv/article_elements_srv.php?action=download_xml&item_id=21139; https://doaj.org/toc/1314-2836; https://doaj.org/toc/1314-2828 |
| DOI: | 10.3897/BDJ.5.e21139 |
| URL الوصول: | https://doaj.org/article/c48fc52b11444671ae24670f76cabb39 |
| رقم الأكسشن: | edsdoj.48fc52b11444671ae24670f76cabb39 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 13142828 13142836 |
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| DOI: | 10.3897/BDJ.5.e21139 |