Abstract:
Numerous organizations, including local governments, health, and medical institutions, and even the WHO, have expressed
concern about the fatality and transmission rates of this illness due to the rapid increase in monkeypox cases around the world.
Monkeypox illness and the COVID-19 virus have a very similar pattern of spread. The number of reported cases worldwide has
increased in recent months, continuing the same trend as COVID-19. The characteristics and signs of the monkeypox virus are similar
to those of any other viral illness. The main signs and symptoms are fever, chills, fatigue, headache, etc. At this juncture, the diagnosis
is a significant challenge. Another challenge is posed by the disease when the obvious symptoms occur like rashes on the skin. The
problem with these specific symptoms is that their appearance resembles other diseases like Chickenpox, Cowpox, and so on. Thus, the
correct classification and proper diagnosis of the disease are tough and strenuous. Thus, for efficient and correct classification of this
severe epidemic, a neural network-based solution is proposed, which classifies the disease at its initial stage with a competent accuracy
rate of 94.38%. the proposed solution is excelling the diagnosis problem by performing efficient classification.
