Classification of Monkeypox Using Greylag Goose Optimization (GGO) Algorithm

After the COVID-19 epidemic, public health awareness increased. A skin viral disease known as monkeypox sparked an emergency alert, leading to numerous reports of infections across numerous European countries. Common symptoms of this disease are fever, high temperatures, and water-filled blisters. This paper presents one of the recent algorithms based on a metaheuristic framework. To improve the performance of monkeypox classification, we introduce the GGO algorithm. Firstly, we employ four pre-trained models (AlexNet, GoogleNet, Resnet-50, and VGG-19) to extract the most common features of monkeypox skin image disease (MSID). Then, we reduce the number of extracted features to select the most distinguishing features for the disease. We make it by using GGO in binary form, which has an average fitness of 0.60068 and a best fitness of 0.50248. Lastly, we apply various optimization algorithms, including the (WWPA) waterwheel plant algorithm, the (DTO) Boosted Dipper Throated Optimization, the (PSO) particle swarm optimizer, the (WAO) whale optimization algorithm, the (GWO) gray wolf optimizer, the (FA) firefly algorithm, and the GGO algorithm, all based on the Convolution Neural Network (CNN), to achieve the best performance. Best Performance is indicated in accuracy and sensitivity; it reached 0.9919 and 0.9895 by GGO. A rigorous statistical analysis test was applied to confirm the validity of our findings. We applied Analysis of Variance ANOVA, and Wilcoxon signed tests, and the results indicated that the value of p was less than 0.005, which strongly supports our hypothesis.