Performance evaluation of Googlenet, Squeezenet, and Resnet50 in the classification of Herbal images
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International Journal of Engineering Trends and Technology (IJETT) |  |
| © 2021 by IJETT Journal | ||
| Volume-69 Issue-3 |
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| Year of Publication : 2021 | ||
| Authors : M.A. Muthiah, E. Logashamugam, N.M. Nandhitha |
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| DOI : 10.14445/22315381/IJETT-V69I3P234 |
Citation
MLA Style: M.A. Muthiah, E. Logashamugam, N.M. Nandhitha "Performance evaluation of Googlenet, Squeezenet, and Resnet50 in the classification of Herbal images" International Journal of Engineering Trends and Technology 69.3(2021):229-232.
APA Style:M.A. Muthiah, E. Logashamugam, N.M. Nandhitha. Performance evaluation of Googlenet, Squeezenet, and Resnet50 in the classification of Herbal images International Journal of Engineering Trends and Technology, 69(3),229-232.
Abstract
Computer-aided classification of medicinal herbs is of major concern in medicinal research. The real challenge lies in the complexity and variability of plants belonging to the same species. Conventional approaches involve feature extraction and classification. Feature extraction is not that effective due to the shift variance of plots. Hence an exhaustive research technique is needed to perform the classification of medical herbs. Convolutional Neural Networks are the recently accepted paradigm for classification with the help of pre-trained neural networks. In this paper feasibility of GoogleNet, SqueezeNet, Resnet 50 for the classification of medicinal herbs is studied. Its is found that Resnet 50 provides high sensitivity for both trained and test dataset.
Reference
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Keywords
GoogleNet, SqueezeNet, Resnet 50, Herbs, Classification, Sensitivity, Specificity