Dear Editor, we would like to share ideas on the publication “Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches [1].” For the purpose of detecting the monkeypox virus, Sitaula and Shahi compared 13 different pre-trained deep learning (DL) models. In order to do this, we first adjusted them by adding universal custom layers for each of them, and we then analyzed the data using four recognized metrics: “Precision”, “Recall”.

“F1-score”, and “Accuracy” [1]. With the aid of our suggested ensemble approach, Sitaula and Shahi's trials on a publicly accessible dataset produced average “Precision”, “Recall”, “F1-score”, and “Accuracy” values of 85.44%, 85.47%, 85.40%, and 87.13%, respectively [1]. Sitaula and Shahi came to the conclusion that the proposed strategy might be used by health professionals for mass screening because of the positive results, which outperformed the most recent methods [1].

We both agree that monkeypox is currently a serious problem, and the disease's global spread has caused concern outside of Africa [2]. Studies like the one in question typically emphasize the disease from the perspectives of men who have sex with men and HIV-positive people (PWH) (MSM). Actually, the syndrome can occur in several populations. It should increase the work's generalizability beyond the demographic of guys who engage in sex with males. According to evidence from Africa, the sickness can affect people of any age or sexual orientation. It's also crucial to understand the abnormal clinical state. Fever and a skin rash may be the first symptoms. However, the disease may occasionally present clinically in an unexpected way [2, 3]. The new method's applicability to a case with an atypical presentation is still up for debate. The current experiment is based on the typical condition, hence, more study is required.