利用深度学习模式诊断中耳疾病

D. Tayal, Neha Srivastava, Urshi Singh
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摘要

近年来,研究重点是开发一种深度学习网络,该网络可以使用鼓膜的图像来识别中耳的情况。自动耳问题诊断尤其有用。即使使用抗生素治疗,中耳炎仍然会导致听力损伤,甚至在几乎所有年龄组中都有听力损失。在诊断过程中对鼓膜的评价和对网络潜在价值的评价构成了耳病诊断的初步检查。本文提出了使用几种深度学习模型识别中耳疾病的策略。深度神经学习辅助耳部状况分析可以通过协助非专家识别中耳炎来增加没有耳鼻喉科医生的人的医疗可及性。这种深度学习网络将能够更精确地诊断中耳问题,并帮助医生分析鼓膜图像。
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Diagnosis of Middle Ear Diseases using Deep Learning Paradigm
In recent years, research has focused on developing a deep learning network that could use images of the ear drum identify conditions in the middle ear. Automatic ear problem diagnosis in particular, can be helpful. Even when antibiotics are used to treat it, otitis media still drives hearing impairment, even loss of hearing in almost all age groups. The evaluation of the tympanic membrane and assessment of the potential value of the network during the diagnostic process constitute the initial examination for the diagnosis of ear sickness. The strategy for identifying middle ear disorders using several deep learning models is proposed in this paper. Deep neural learning aid in the analysis of ear condition may increase medical accessibility for persons without access to otolaryngologists by assisting non- specialists in recognizing otitis media. This deep learning network will be able to diagnose the middle ear problems more precisely and help doctors analyse images of the tympanic membrane.
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