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Deep Learning and Handcrafted Features for Virus Image Classification
| Content Provider | MDPI |
|---|---|
| Author | Nanni, Loris Luca, Eugenio De Facin, Marco Ludovico Maguolo, Gianluca |
| Copyright Year | 2020 |
| Description | In this work, we present an ensemble of descriptors for the classification of virus images acquired using transmission electron microscopy. We trained multiple support vector machines on different sets of features extracted from the data. We used both handcrafted algorithms and a pretrained deep neural network as feature extractors. The proposed fusion strongly boosts the performance obtained by each stand-alone approach, obtaining state of the art performance. |
| Starting Page | 143 |
| e-ISSN | 2313433X |
| DOI | 10.3390/jimaging6120143 |
| Journal | Journal of Imaging |
| Issue Number | 12 |
| Volume Number | 6 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-12-21 |
| Access Restriction | Open |
| Subject Keyword | Journal of Imaging Artificial Intelligence Virus Classification Texture Descriptors Deep Learning Local Binary Patterns Ensemble of Descriptors |
| Content Type | Text |
| Resource Type | Article |
