چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | علوم پزشکی کرمانشاه |
| کد مقاله | 19563 |
| عنوان فارسی مقاله | تشخیص پنوموتوراکس در تصاویر سی تی اسکن با استفاده از الگوریتم های یادگیری ماشین و ویژگی های رادیومیکس |
| عنوان لاتین مقاله | Diagnosis of pneumothorax in CT scan images using machine learning algorithms and radiomics features |
| نوع ارائه | سخنرانی |
| عنوان کنگره / همایش | سومین کنگره بین المللی انجمن علمی دانشجویان رادیولوژی کشور |
| نوع کنگره / همایش | بین المللی |
| کشور محل برگزاری کنگره/ همایش | ایران |
| شهر محل برگزاری کنگره/ همایش | سبزوار |
| سال انتشار/ ارائه شمسی | 1402 |
| سال انتشار/ارائه میلادی | 2023 |
| تاریخ شمسی شروع و خاتمه کنگره/همایش | 1402/07/11 الی 1402/07/12 |
| آدرس لینک مقاله/ همایش در شبکه اینترنت | The third international congress of the scientific society of radiology students of the country |
| آدرس علمی (Affiliation) نویسنده متقاضی | 4.0000 |
| نویسنده | نفر چندم مقاله |
|---|---|
| کریم خوش گرد | دوم |
| هانیه علی میری ده باغی | اول |
| حمید شرینی | سوم |
| سمیرا جعفری خیرآبادی | چهارم |
| عنوان | متن |
|---|---|
| کلمات کلیدی | Pneumothorax, CT scan, Machine Learning , Artifitial Intelligenc |
| چکیده | Imaging in the emergency department is a time-dependent process. The development of artificial intelligence models can improve the performance of any diagnostic system by minimizing diagnostic errors and increasing the speed of image interpretation by radiologists. The purpose of this study is to create an intelligent model for pneumothorax detection in CT scan images using radiomics features and implementing machine learning algorithms. The data used in this study was extracted from the files of 175 patients suspected of pneumothorax. The collected images were pre-processed in the Matlab software. Then the machine learning algorithms including Gradient Tree Boosting (GBM), eXtreme Gradient Boosting (XGBOOST) and Light GBM (LGBM) were used to classify the images. Various evaluation criteria such as precision, accuracy, specificity, sensitivity, F1 score, the area under the ROC curve and misclassification were calculated to evaluate the performance of these models. According to the calculated evaluation criteria, for the Light GBM model, the accuracy, precision, specificity and F1 scores were 0.98979, 0.99559, 0.98435 and 0.99430, respectively. These findings indicate the better performance of the Light GBM model compared to other models. The Light GBM model with a sensitivity value of 0.99763 had the best performance among these algorithms. The obtained results showed that the machine learning algorithms used in this research can accurately identify healthy and pneumothorax images and thus facilitate and accelerate the process of diagnosis and treatment of this complication |
| متن مقاله | |
| نتیجه مقاله | The obtained results showed that the machine learning algorithms used in this research can accurately identify healthy and pneumothorax images and thus facilitate and accelerate the process of diagnosis and treatment of this complication |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| Hanieh Alimiri Dehbaghi.pdf | 1402/09/09 | 176428 | دانلود |
| article.PNG | 1402/09/09 | 226780 | دانلود |
| call for paper.PNG | 1402/09/09 | 743864 | دانلود |
