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Deep Learning Based Bacteria Classification

机译:基于深度学习的细菌分类

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Deep learning, also called hierarchical learning, aims to analyze the structure of input data from simple to complex with the help of multi-layer neural networks. Particularly the distinctive features obtained by Convolutional Neural Network (CNN) make the classification process much easier. The methods based on classification with deep learning are used in many different areas in parallel with the development of technology. Medicine, military, education, industry, trade etc. these methods are used for different purposes in the field. Deep learning based bacteria classification was performed in the study. The DIBaS data set was used as the data set. The study was performed using VggNet and AlexNet training model in MATLAB environment. For classification, 33 different bacteria species were used and 98,25% for VggNet and 97,53% for AlexNet.
机译:深度学习,也称为分层学习,旨在利用多层神经网络的帮助分析简单到复杂的输入数据的结构。特别是通过卷积神经网络(CNN)获得的独特特征使分类过程更容易。基于深度学习分类的方法在许多不同的区域与技术的发展并行。医学,军事,教育,工业,交易等这些方法用于该领域的不同目的。在研究中进行了深入的基于学习的细菌分类。 DIBAS数据集用作数据集。该研究是在Matlab环境中使用Vggnet和AlexNet培训模型进行的。对于分类,使用33种不同的细菌物种,验证了33种不同的细菌物种,验证了98,25%,验证了97,53%。

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