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Different works have introduced by several researchers and scientists for the processing of virus as well as other images for wide variety of applications in the real world scenario [1–34, 35–55]. Some of the works are described as follows. Singh et al. [2] focus on the review of several ML as well as image processing techniques for the detection and classification of paddy leaf diseases. Al-Kasassbeh et al. [5] focus on the feature selection mechanism by the help of ML-based approach for the classification of malware. Yang et al. [6] focus on a sequence embedding-based ML mechanism for the prediction of human-virus protein–protein interactions. Dey et al. [7] focus on ML-based techniques for sequence based prediction of viral host interactions between human proteins and SARS-CoV-2. Karanja et al. [9] focus on ML-based techniques as well as image texture features for the analysis of internet of things malware. Muda et al. [14] focus on the k-means clustering as well as NB classification mechanism for intrusion detection. Trishan et al. [17] focus on ML-based classification such as NB, k-nearest and random forest to detect Hepatitis A, B, C and E viruses. Kaur [19] focuses on the ML-based approaches such as kNN and NB for the detection of fraud associated with credit card. Goyal [20] focuses on a NB model that is based on enhanced kNN classification mechanism for the prediction of breast cancer. Wahid et al. [22] focus on the performance analysis of several ML-based techniques for the classification of microscopic bacteria images. Ito et al. [27] focus on convolutional NN mechanism for the detection of virus particle in transmission electron microscopy (TEM) images. Devan et al. [28] focus on transfer learning mechanism to detect herpesvirus capsids by considering several TEM images.