# Deep learning and hand-crafted features for virus image classification

Source: [Deep learning and hand-crafted features for virus image classification](<https://www.sota2.com/research/paper/68fa6b7a1e209131cea2e6b1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:49:06.844Z

Authors: Loris Nanni, Eugenio De Luca, Marco Ludovico Facin, Gianluca Maguolo

Published: 2020-11-11T23:46:16.000Z

## Abstract

In this work, we present an ensemble of descriptors for the classification of transmission electron microscopy images of viruses. We propose to combine handcrafted and deep learning approaches for virus image classification. The set of handcrafted is mainly based on Local Binary Pattern variants, for each descriptor a different Support Vector Machine is trained, then the set of classifiers is combined by sum rule. The deep learning approach is a densenet201 pretrained on ImageNet and then tuned in the virus dataset, the net is used as features extractor for feeding another Support Vector Machine, in particular the last average pooling layer is used as feature extractor. Finally, classifiers trained on handcrafted features and classifier trained on deep learning features are combined by sum rule. The proposed fusion strongly boosts the performance obtained by each stand-alone approach, obtaining state of the art performance.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2011.06123>)

[PDF](<https://arxiv.org/pdf/2011.06123.pdf>)