Classification of Monkeypox Images Based on Transfer Learning and the Al-Biruni Earth Radius Optimization Algorithm

The world is still trying to recover from the devastation caused by wide spread of COVID-19, and now monkeypox virus threatens becoming a worldwide pandemic. Although not as lethal or infectious numerous countries report new cases daily. Thus, it surprising that necessary precautions have been taken, will be if another pandemic occurs. Machine learning has recently shown tremendous promise in image-based diagnosis, including cancer detection, tumor cell identification, COVID-19 patient detection. Therefore, similar application may implemented diagnose invades human skin. An image can acquired utilized further condition. In this paper, two algorithms are proposed for improving classification accuracy images. based on transfer feature extraction meta-heuristic optimization selection parameters multi-layer neural network. GoogleNet deep network adopted extraction, Al-Biruni Earth radius algorithm, sine cosine particle swarm algorithm. Based these algorithms, binary hybrid algorithm selection, along with optimizing To evaluate publicly available dataset employed. assessment was performed terms ten evaluation criteria. addition, set statistical tests conducted measure effectiveness, significance, robustness algorithms. results achieved confirm superiority effectiveness methods compared other methods. average 98.8%.