Navigating otitis media classification: a comprehensive survey and analysis

An ear infection known as otitis media (OM) affects a large proportion of the world's population. For effective diagnosis and therapy, OM must be categorised accurately and promptly. Recent developments in artificial intelligence (AI) have made it possible to classify OM in novel ways and have improved the skills of healthcare practitioners. This survey paper seeks to present a thorough overview of the recent strategies and techniques for OM categorisation while illuminating the accompanying difficulties. To give a comprehensive knowledge of the development of OM classification systems, each category is extensively analysed. This survey article serves as a significant resource for scholars, healthcare professionals, and stakeholders in the area by compiling the recent knowledge on OM categorisation. It emphasises how crucial it is to use computer vision and machine learning (ML) approaches to get beyond the difficulties posed by manual otoscopy, resulting in increased efficacy and accuracy in the identification of OM.