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
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
| 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 |
|
| 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. |
| نام فایل |
تاریخ درج فایل |
اندازه فایل |
دانلود |
| 7a0efa3b-f3c5-4d14-a68c-7bb6f1b16b24.pdf | 1401/02/27 | 1301825 | دانلود |