Title: Navigating otitis media classification: a comprehensive survey and analysis

Authors: S. Mary Selvi; Velappan Subha; A. Manivanna Boopathi; S. Thanu

Addresses: Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, 627 012, Tamil Nadu, India ' Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, 627 012, Tamil Nadu, India ' Department of Electrical and Electronics Engineering, Sethu Institute of Technology, Kariapatti, Tamilnadu, India ' Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, India

Abstract: 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.

Keywords: otitis media; OM; ear infection; computer vision; machine learning; ML; classification.

DOI: 10.1504/IJIEI.2026.154023

International Journal of Intelligent Engineering Informatics, 2026 Vol.14 No.2, pp.173 - 204

Received: 26 Jun 2024
Accepted: 09 Oct 2024
Published online: 10 Jun 2026 *

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