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dc.contributor.authorElgaed, Mohammed
dc.contributor.authorElgdamsi, Khaled
dc.contributor.authorGanoun, Ali
dc.date.accessioned2020-12-09T21:03:20Z
dc.date.available2020-12-09T21:03:20Z
dc.date.issued2020-12-03
dc.identifier.urihttp://dspace.elmergib.edu.ly/xmlui/handle/123456789/222
dc.description.abstractBacteria Classification using computer-aided methods makes the identification and recognition processes more automatic and thus greatly reduces the time needed for classification. In this paper, we explored an approach to automating the process of classifying bacteria with the use of deep Convolutional Neural Network (CNN). CNNs is one of deep machine learning methods that mimics the connectivity pattern between visual cortex neurons. It can extract hierarchical image feature representations based on multi-layer processing. The ‘transfer learning’ approach was used to retrain a famous convolutional neural network model with a dataset of 152 images of 7 different bacteria species. The retrained model has been able to recognize and classify all 7 different species of bacteria with very high accuracy.en_US
dc.language.isoenen_US
dc.subjectbacteria classification, deep learning, convolution neural network, transfer learning, computer Visionen_US
dc.titleClassifying Various Bacteria Genera by Transfer Learning Modelen_US
dc.typeArticleen_US


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