2023/10/02 by Alireza Farzipour, Farzipour, Alireza, Roya Elmi +3
Biochemistry, Genetics and Molecular Biology · Engineering · Immunology and Microbiology · Medicine · #Artificial Intelligence (cs.AI) #Artificial intelligence #Biology #Cell Image Analysis Techniques #Computer science #Computers and Society (cs.CY) #FOS: Computer and information sciences #Image Processing Techniques and Applications #Machine Learning (cs.LG) #Medical diagnosis #Medicine #Monkeypox #Pathology #Poxvirus research and outbreaks
paper · pdf · doi:10.48550/arxiv.2310.19801
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/10/02 · openalex created_date 2023/11/02 · openalex updated_date 2026/07/28
Monkeypox is a zoonotic disease. About 87000 cases of monkeypox were confirmed by the World Health Organization until 10th June 2023. The most prevalent methods for identifying this disease are image-based recognition techniques. Still, they are not too fast and could only be available to a few individuals. This study presents an independent application named SyMPox, developed to diagnose Monkeypox cases based on symptoms. SyMPox utilizes the robust XGBoost algorithm to analyze symptom patterns and provide accurate assessments. Developed using the Gradio framework, SyMPox offers a user-friendly platform for individuals to assess their symptoms and obtain reliable Monkeypox diagnoses.