Recognizing and classifying viruses is fundamental to the medical field for both diagnosis and research. Since this task requires highly qualified medical-staff, there is a growing interest in making this process automatic. Such images can be acquired using Transmission Electronic Microscopy(TEM), which is currently not used in clinical practice, and that could be an innovative diagnostic tool.

The main difficulty in classifying viruses is their large number, due to the introduction of the DNA sequencing technique that made the number of classified viruses grow exponentially. Besides, other factors create an accurate virus taxonomy very complex: their replication and genetic heritage.

The proposed network is validated through extensive experiments on a TEM virus image dataset. The model evaluated on the test dataset parameter given in below. The model can classify more than 204 images per second.

• Accuracy: 0.9216

• Sensitivity: 0.9189

• Specificity: 0.9938

• Precision: 0.9064,

• FalsePositiveRate(Type-1 Error): 0.0062

• F1Score: 0.9091

• Latency: 0.0049s

The proposed automated method contributes to the development of medical virology, which provides virologists with a high-accuracy approach to recognize viruses and assist in diagnosing viruses.