An Empirical Study on Classification of Monkeypox Skin Lesion Detection

INTRODUCTION: After the covid-19 outbreak, Monkeypox has become a global pandemic putting people’s lives in jeopardy. major concern 40+ countries apart from Africa as scientists are struggling to clinically diagnose virus it looks similar with chickenpox and measles. As part of our research, we found that get tested result monkey pox through polymerase chain reaction (PCR) test would take 3-4 days which is lengthy process.OBJECTIVES: The objective this paper provide rapid identification solution can instantly detect monkeypox help computer vision architectures. This be considered for preliminary examination skin lesions victim isolate themselves so they cautious stop spreading virus. METHODS: Many studies have been conducted identify Deep Learning models but study, compare results obtained by deep learning CNN AlexNet, GoogLeNet using transfer approach determine efficient model[2].RESULTS: Testing algorithms changing batch sizes number epochs highest accuracy 83.61% AlexNet 82.64% GoogLeNet.CONCLUSION: was outperforming architecture terms validation thus providing better results.