دانلود مقاله انگلیسی رایگان:تشخیص خودکار بیماری گوش با استفاده از یادگیری عمیق گروه با یک پایگاه داده بزرگ تصویر otoendoscopy - 2019
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  • Automated diagnosis of ear disease using ensemble deep learning with a big otoendoscopy image database Automated diagnosis of ear disease using ensemble deep learning with a big otoendoscopy image database
    Automated diagnosis of ear disease using ensemble deep learning with a big otoendoscopy image database

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Automated diagnosis of ear disease using ensemble deep learning with a big otoendoscopy image database


    ترجمه فارسی عنوان مقاله:

    تشخیص خودکار بیماری گوش با استفاده از یادگیری عمیق گروه با یک پایگاه داده بزرگ تصویر otoendoscopy


    منبع:

    Sciencedirect - Elsevier - EBioMedicine 45 (2019) 606–614


    نویسنده:

    Dongchul Cha a,1, Chongwon Pae b,c,d,1, Si-Baek Seong b,c, Jae Young Choi a,c,⁎⁎, Hae-Jeong Park b,c,d,⁎


    چکیده انگلیسی:

    Background: Ear and mastoid disease can easily be treated by early detection and appropriate medical care. However, short of specialists and relatively lowdiagnostic accuracy calls for a newway of diagnostic strategy, inwhich deep learning may play a significant role. The current study presents a machine learning model to automatically diagnose ear disease using a large database of otoendoscopic images acquired in the clinical environment. Methods: Total 10,544 otoendoscopic images were used to train nine public convolution-based deep neural networks to classify eardrum and external auditory canal features into six categories of ear diseases, covering most ear diseases (Normal, Attic retraction, Tympanic perforation, Otitis externa±myringitis, Tumor). After evaluating several optimization schemes, two best-performingmodelswere selected to compose an ensemble classifier, by combining classification scores of each classifier. Findings: According to accuracy and training time, transfer learning models based on Inception-V3 and ResNet101 were chosen and the ensemble classifier using the two models yielded a significant improvement over each model, the accuracy of which is in average 93·67% for the 5-folds cross-validation. Considering substantial data-size dependency of classifier performance in the transfer learning, evaluated in this study, the high accuracy in the current model is attributable to the large database. Interpretation: The current study is unprecedented in terms of both disease diversity and diagnostic accuracy, which is compatible or even better than an average otolaryngologist. The classifier was trainedwith data in a various acquisition condition,which is suitable for the practical environment. This study shows the usefulness of utilizing a deep learning model in the early detection and treatment of ear disease in the clinical situation. Fund: This research was supported by Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT(NRF-2017M3C7A1049051).
    Keywords: Convolutional neural network | Deep learning | Otoendoscopy | Tympanic membrane | Ear disease | Ensemble learning


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 9
    حجم فایل: 2819 کیلوبایت

    قیمت: رایگان


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