Otitis media (OM) is an inflammation of the middle ear, often associated with fluid accumulation and characterized by symptoms such as ear pain, fever, and impaired hearing. Timely and accurate diagnosis of OM is essential to facilitate prompt treatment and mitigate the risk of complications such as hearing loss or chronic infection, particularly in regions with limited access to healthcare professionals. In this study, we introduce an advanced computational model for automated OM diagnosis, utilizing the vision transformer (ViT) architecture to extract highly discriminative features from otoscope images. The proposed approach employs a grid search optimization algorithm in combination with a support vector machine (SVM) classifier to accurately recognize different types of OM based on deep feature representations. All experiments were conducted using a publicly accessible Ear Imagery dataset containing 880 otoscope images, categorized into four distinct classes. As a result, the proposed model demonstrated remarkable efficacy, achieving an impressive accuracy rate of 99.37%. It successfully classified all OM types. At its core, the emergence of advanced computational models in healthcare represents a transformative leap that promises to close gaps in access to medical expertise and revolutionize diagnostic practices. Harnessing the power of machine learning and leveraging vast datasets, these models offer unprecedented accuracy and efficiency, paving the way for early intervention and improving patient outcomes on a global scale.
Computerized otoscopy image-based artificial intelligence model utilizing deep features provided by vision transformer, grid search optimization, and support vector machine for otitis media diagnosis / Cömert, Zafer; Sbrollini, Agnese; Demircan, Furkancan; Burattini, Laura. - In: NEURAL COMPUTING & APPLICATIONS. - ISSN 0941-0643. - 36:(2024), pp. 23113-23129. [10.1007/s00521-024-10457-y]
Computerized otoscopy image-based artificial intelligence model utilizing deep features provided by vision transformer, grid search optimization, and support vector machine for otitis media diagnosis
Sbrollini, Agnese;Burattini, Laura
2024-01-01
Abstract
Otitis media (OM) is an inflammation of the middle ear, often associated with fluid accumulation and characterized by symptoms such as ear pain, fever, and impaired hearing. Timely and accurate diagnosis of OM is essential to facilitate prompt treatment and mitigate the risk of complications such as hearing loss or chronic infection, particularly in regions with limited access to healthcare professionals. In this study, we introduce an advanced computational model for automated OM diagnosis, utilizing the vision transformer (ViT) architecture to extract highly discriminative features from otoscope images. The proposed approach employs a grid search optimization algorithm in combination with a support vector machine (SVM) classifier to accurately recognize different types of OM based on deep feature representations. All experiments were conducted using a publicly accessible Ear Imagery dataset containing 880 otoscope images, categorized into four distinct classes. As a result, the proposed model demonstrated remarkable efficacy, achieving an impressive accuracy rate of 99.37%. It successfully classified all OM types. At its core, the emergence of advanced computational models in healthcare represents a transformative leap that promises to close gaps in access to medical expertise and revolutionize diagnostic practices. Harnessing the power of machine learning and leveraging vast datasets, these models offer unprecedented accuracy and efficiency, paving the way for early intervention and improving patient outcomes on a global scale.File | Dimensione | Formato | |
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