Enhancing Monkeypox Detection through Data Analytics: A Comparative Study of Machine and Deep Learning Techniques - Kinjal A. Patel; Asadi Srinivasulu; Kuntesh Jani; Goddindla Sreenivasulu (Advances in Engineering and Intelligence Systems)
Monkeypox detection is a challenging task due to the disease's resemblance to other viral infections such as smallpox and chickenpox. This paper presents a comprehensive study that investigates the efficacy of machine and deep learning techniques in detecting monkeypox. The research utilizes monkeypox detection data to train and assess the performance of various machine learning and deep learning models. The results demonstrate that deep learning models outperform traditional machine learning approaches in accurately identifying cases of monkeypox. The study emphasizes the significance of machine and deep learning techniques for enhancing the accuracy and speed of monkeypox detection. The findings highlight the potential of these advanced algorithms to aid in controlling outbreaks and curbing the transmission of the disease. By leveraging the power of data analytics, healthcare professionals can quickly and accurately identify cases of monkeypox, facilitating timely intervention and effective management strategies. This research contributes to the field of infectious disease surveillance by showcasing the advantages of employing cutting-edge machine and deep learning techniques for monkeypox detection. The study serves as a foundation for further research, encouraging the exploration of novel methodologies and the development of intelligent systems to assist healthcare providers in promptly identifying and responding to monkeypox outbreaks. Ultimately, this work aims to improve public health outcomes and mitigate the impact of monkeypox on affected populations.