Deep Learning Approaches for Otitis Media Classification: A Comprehensive Survey
Keywords:
Otitis Media, Ear Infection, Deep Learning, ClassificationAbstract
Middle ear infections known as Otitis Media (OM) are classified as inflammatory diseases. It is one of the most prevalent illnesses affecting young children and the second-largest cause of hearing loss. The OM can go away on its own without creating any problems, but it can also result in hearing loss and have long-lasting effects. Diagnostic techniques include tympanometry, audiometry, and (pneumatic) otoscopy. Accurate and timely classification of OM is essential for effective diagnosis and treatment. Recent advancements in Artificial Intelligence (AI) have introduced innovative methods for classifying Otitis Media, significantly enhancing diagnostic accuracy and supporting healthcare professionals in clinical settings. The primary objective of this survey is to provide a comprehensive overview of recent strategies and techniques for the classification of Otitis Media using artificial intelligence approaches. This includes an in-depth analysis of existing OM classification systems, highlighting the evolution of methods and examining their respective strengths and limitations. This survey article consolidates recent insights on Otitis Media classification, serving as a valuable resource for researchers, clinicians, and healthcare stakeholders. It emphasises how crucial it is to use Deep Learning (DL) approaches to get beyond the difficulties posed by manual otoscopy, resulting in increased efficacy and accuracy in the identification of OM.
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