MedElevator Войти в исследования
← Поиск публикаций

DOI 10.3390/ijerph16101688

Медицинская библиотека · International journal of environmental research and public health · 2019

Screening and Diagnosis of Chronic Pharyngitis Based on Deep Learning.

Li Zhichao, Huang Jilin, Hu Zhiping

СтатьяПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

Chronic pharyngitis is a common disease, which has a long duration and a wide range of onset. It is easy to misdiagnose by mistaking it with other diseases, such as chronic tonsillitis, by using common diagnostic methods. In order to reduce costs and avoid misdiagnosis, the search for an affordable and rapid diagnostic method is becoming more and more important for chronic pharyngitis research. Speech disorder is one of the typical symptoms of patients with chronic pharyngitis. This paper introduces a convolutional neural network model for diagnosis based on the typical symptom of speech disorder. First of all, the voice data is converted into a speech spectrogram, which can better output the speech characteristic information and lay a foundation for computer diagnosis and discrimination. Second, we construct a deep convolutional neural network for the diagnosis of chronic pharyngitis through the design of the structure, the design of the network layer, and the description of the function. Finally, we perform a parameter optimization experiment on the convolutional neural network and judge the recognition efficiency of chronic pharyngitis. The results show that the convolutional neural network has a high recognition rate for patients with chronic pharyngitis and has a good diagnostic effect.

Для цитирования: Li Zhichao, Huang Jilin, Hu Zhiping. Screening and Diagnosis of Chronic Pharyngitis Based on Deep Learning.. International journal of environmental research and public health. 2019. DOI: 10.3390/ijerph16101688.

Предметные рубрики MeSH

Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Тип публикации указан по данным источника. Уровень доказательности автоматически не присваивается.