Classification of mastoid air cells by CT scan images using deep learning method

Classification of mastoid air cells by CT scan images using deep learning method


چاپ صفحه
پژوهان
صفحه نخست سامانه
چکیده مقاله
چکیده مقاله
نویسندگان
نویسندگان
دانلود مقاله
دانلود مقاله
دانشگاه علوم پزشکی تبریز
دانشگاه علوم پزشکی تبریز

نویسندگان: محمد خسروی , یلدا جباری مقدم , احمد کشتکار , جواد جلیلی , حمید طایفی نصرآبادی , مهداد اسمعیلی

کلمات کلیدی: Keywords: Convolutional neural network, Deep learning, CT scan, Ear disease, Mastoid pneumatization

نشریه: 0 , 62 , 9 , 2022

اطلاعات کلی مقاله
hide/show

نویسنده ثبت کننده مقاله یلدا جباری مقدم
مرحله جاری مقاله تایید نهایی
دانشکده/مرکز مربوطه دانشکده پزشکی
کد مقاله 78726
عنوان فارسی مقاله Classification of mastoid air cells by CT scan images using deep learning method
عنوان لاتین مقاله Classification of mastoid air cells by CT scan images using deep learning method
ناشر 6
آیا مقاله از طرح تحقیقاتی و یا منتورشیپ استخراج شده است؟ خیر
عنوان نشریه (خارج از لیست فوق) Journal of Big Data
نوع مقاله Original Article
نحوه ایندکس شدن مقاله ایندکس شده سطح یک – ISI - Web of Science
آدرس لینک مقاله/ همایش در شبکه اینترنت

خلاصه مقاله
hide/show

Purpose: Mastoid abnormalities show different types of ear illnesses, however inadequacy of experts and low accuracy of diagnostic demand a new approach to detect these abnormalities and reduce human mistakes. The manual analysis of mastoid CT scans is time-consuming and labor-intensive. In this paper the first and robust deep learning-based approaches is introduced to diagnose mastoid abnormalities using a large database of CT images obtained in the clinical center with remarkable accuracy. Methods: In this paper, mastoid abnormalities are classified using the Xception based Convolutional Neural Network (CNN) model, with optimizer Adamax into five categories (Complete pneumatized, Opacification in pneumatization, Partial pneumatization, Opacification in partial pneumatization, None pneumatized). For this reason, a total of 24,800 slides of 152 patients were selected that include the mastoid from most upper to the lowest part of the middle ear cavity to complete the construction of the proposed deep neural network model. Results: The proposed model had the best accuracy of 87.80% (based on grader 1) and 88.44% (based on grader 2) on the 20th epoch and 87.70% (based on grader 1) and 87.56% (based on grader 2) on average and also significantly faster than other types of implemented architectures in terms of the computer running time (in seconds). The 99% confidence interval of the average accuracy was 0.012 which means that the true accuracy is 87.80% and 87.56%±1.2% that indicates the power of the model. Conclusions: The manual analysis of ear cavity CT scans is often time-consuming and prone to errors due to various inter- or intra operator variability studies. The proposed method can be used to automatically analyze the middle ear cavity to classify mastoid abnormalities, which is markedly faster than most types of models with the highest accuracy.

نویسندگان
hide/show

نویسنده نفر چندم مقاله
محمد خسرویاول
یلدا جباری مقدمدوم
احمد کشتکارچهارم
جواد جلیلیپنجم
حمید طایفی نصرآبادیششم
مهداد اسمعیلیسوم

لینک دانلود مقاله
hide/show

نام فایل تاریخ درج فایل اندازه فایل دانلود
7a0efa3b-f3c5-4d14-a68c-7bb6f1b16b24.pdf1401/02/271301825دانلود