Image dataset on the Chinese medicinal blossoms for classification through convolutional neural network

Tree blossoms have been widely used on the prevention and treatment of a variety of diseases in traditional Chinese medicine for thousand years [1,2]. The growth of flowers is not only for their ornamental value, but also for nutritional, medicinal, cooking, cosmetic and aromatic properties. They ar...

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Autores principales:	Mei-Ling Huang, Yi-Xuan Xu, Yu-Chieh Liao
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Chinese medicinal blossom Classification Data augmentation Deep learning Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/ff1f748afc7a4755b773c82eea9d3efc
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id	oai:doaj.org-article:ff1f748afc7a4755b773c82eea9d3efc
record_format	dspace
spelling	oai:doaj.org-article:ff1f748afc7a4755b773c82eea9d3efc2021-12-04T04:34:39ZImage dataset on the Chinese medicinal blossoms for classification through convolutional neural network2352-340910.1016/j.dib.2021.107655https://doaj.org/article/ff1f748afc7a4755b773c82eea9d3efc2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352340921009306https://doaj.org/toc/2352-3409Tree blossoms have been widely used on the prevention and treatment of a variety of diseases in traditional Chinese medicine for thousand years [1,2]. The growth of flowers is not only for their ornamental value, but also for nutritional, medicinal, cooking, cosmetic and aromatic properties. They are a rich source of many compounds, which play an important role in various metabolic processes of the human body [3]. Edible flowers can promote the global demand for more attractive and delicious food, and can improve the nutritional content of gourmet food [4]. Flowers are beneficial for anti-anxiety, anti-cancer, anti-inflammatory, antioxidant, diuretic and immune-modulator, etc. It is very important to identify edible flowers correctly, because only a few are edible [5].The shapes or colors of different flowers may be very similar. Visual evaluation is one of the classification methods, but it is error-prone and time-consuming [6]. Flowers are divided into flowers from herbaceous plants (flower) and flower trees (blossom). Now there is a public herbaceous flower dataset [7], but lack of dataset for Chinese medicinal blossoms. This article presents and establishes the dataset for twelve most commonly and economically valuable blossoms used in traditional Chinese medicine. The dataset provide a collection of blossom images on traditional Chinese herbs help Chinese pharmacist to classify the categories of Chinese herbs. In addition, the dataset can serve as a resource for researchers who use different algorithms of machine learning or deep learning for image segmentation and image classification.Mei-Ling HuangYi-Xuan XuYu-Chieh LiaoElsevierarticleChinese medicinal blossomClassificationData augmentationDeep learningComputer applications to medicine. Medical informaticsR858-859.7Science (General)Q1-390ENData in Brief, Vol 39, Iss , Pp 107655- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chinese medicinal blossom Classification Data augmentation Deep learning Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
spellingShingle	Chinese medicinal blossom Classification Data augmentation Deep learning Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390 Mei-Ling Huang Yi-Xuan Xu Yu-Chieh Liao Image dataset on the Chinese medicinal blossoms for classification through convolutional neural network
description	Tree blossoms have been widely used on the prevention and treatment of a variety of diseases in traditional Chinese medicine for thousand years [1,2]. The growth of flowers is not only for their ornamental value, but also for nutritional, medicinal, cooking, cosmetic and aromatic properties. They are a rich source of many compounds, which play an important role in various metabolic processes of the human body [3]. Edible flowers can promote the global demand for more attractive and delicious food, and can improve the nutritional content of gourmet food [4]. Flowers are beneficial for anti-anxiety, anti-cancer, anti-inflammatory, antioxidant, diuretic and immune-modulator, etc. It is very important to identify edible flowers correctly, because only a few are edible [5].The shapes or colors of different flowers may be very similar. Visual evaluation is one of the classification methods, but it is error-prone and time-consuming [6]. Flowers are divided into flowers from herbaceous plants (flower) and flower trees (blossom). Now there is a public herbaceous flower dataset [7], but lack of dataset for Chinese medicinal blossoms. This article presents and establishes the dataset for twelve most commonly and economically valuable blossoms used in traditional Chinese medicine. The dataset provide a collection of blossom images on traditional Chinese herbs help Chinese pharmacist to classify the categories of Chinese herbs. In addition, the dataset can serve as a resource for researchers who use different algorithms of machine learning or deep learning for image segmentation and image classification.
format	article
author	Mei-Ling Huang Yi-Xuan Xu Yu-Chieh Liao
author_facet	Mei-Ling Huang Yi-Xuan Xu Yu-Chieh Liao
author_sort	Mei-Ling Huang
title	Image dataset on the Chinese medicinal blossoms for classification through convolutional neural network
title_short	Image dataset on the Chinese medicinal blossoms for classification through convolutional neural network
title_full	Image dataset on the Chinese medicinal blossoms for classification through convolutional neural network
title_fullStr	Image dataset on the Chinese medicinal blossoms for classification through convolutional neural network
title_full_unstemmed	Image dataset on the Chinese medicinal blossoms for classification through convolutional neural network
title_sort	image dataset on the chinese medicinal blossoms for classification through convolutional neural network
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/ff1f748afc7a4755b773c82eea9d3efc
work_keys_str_mv	AT meilinghuang imagedatasetonthechinesemedicinalblossomsforclassificationthroughconvolutionalneuralnetwork AT yixuanxu imagedatasetonthechinesemedicinalblossomsforclassificationthroughconvolutionalneuralnetwork AT yuchiehliao imagedatasetonthechinesemedicinalblossomsforclassificationthroughconvolutionalneuralnetwork
_version_	1718373009358585856

Image dataset on the Chinese medicinal blossoms for classification through convolutional neural network

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