چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| نویسنده ثبت کننده مقاله | ناهیده قره آغاجی |
| مرحله جاری مقاله | تایید نهایی |
| دانشکده/مرکز مربوطه | دانشکده پیراپزشکی |
| کد مقاله | 86674 |
| عنوان فارسی مقاله | Role of Artificial Intelligence in Grading Gliomas Using the EfficientNetB0 Model and MRI Datasets |
| عنوان لاتین مقاله | Role of Artificial Intelligence in Grading Gliomas Using the EfficientNetB0 Model and MRI Datasets |
| نوع ارائه | پوستر |
| عنوان کنگره / همایش | 25TH NATIONAL AND 11TH INTERNATIONAL ANNUAL CONGRESS ON RESEARCH AND TECHNOLOGY OF IRANIAN MEDICAL SCIENCES STUDENTS |
| نوع کنگره / همایش | بین المللی |
| کشور محل برگزاری کنگره/ همایش | Iran (Islamic Republic) |
| شهر محل برگزاری کنگره/ همایش | ارومیه |
| سال انتشار/ ارائه شمسی | 1403 |
| سال انتشار/ارائه میلادی | 2024 |
| تاریخ شمسی شروع و خاتمه کنگره/همایش | 1403/06/15 الی 1403/06/17 |
| آدرس لینک مقاله/ همایش در شبکه اینترنت | https://ibj.pasteur.ac.ir/article-1-4703-en.pdf |
| آدرس علمی (Affiliation) نویسنده متقاضی | Department of Radiology, Faculty of Apllied Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran |
| نویسنده | نفر چندم مقاله |
|---|---|
| ناهیده قره آغاجی | اول |
| مریم باقرپور | دوم |
| عنوان | متن |
|---|---|
| کلمات کلیدی | Artificial intelligence, Glioma, Magnetic resonance imaging |
| خلاصه مقاله | Introduction: Gliomas are primary brain tumors originating from glial cells with various aggressiveness. The precise grading of gliomas is crucial for planning effective treatment strategies and conducting ongoing patient evaluations. Histopathological grading of gliomas is expensive and time-consuming. Nowadays, the combination of artificial intelligence (AI) algorithms and medical imaging techniques, especially magnetic resonance imaging (MRI), has led to provide non-invasive, safe, rapid, and cost-effective diagnostic methods for grading gliomas. This review highlights how AI plays a key role in accurately grading gliomas. Search Strategy: We searched keywords such as “artificial intelligence”, “gliomas”, “grading”, and “MRI” in the PubMed, Google scholar, Elsevier and IEEE. The most current and relevant articles were extracted and reviewed. Then grading gliomas tumors with an AI algorithm using MRI images was evaluated. Results: The study findings suggest that utilizing AI algorithms and MRI image datasets for grading gliomas yields favorable results with high specificity, sensitivity, and accuracy. The researchers studied new automated detection and classification models in AI along with MRI datasets. Among AI models, EfficientNetB0 is one of the most effective models, achieving over 0.98 on the validation set for specificity, sensitivity, and accuracy. The model attained high accuracy with fewer parameters and reduced computational costs compared to traditional convolutional neural networks. Additionally, its scalability makes it versatile and adaptable to various requirements and constraints. Conclusion and Discussion: MRI is a highly precise method for detecting tumors like gliomas compared to current techniques. AI models, especially EfficientNetB0, with excellent specificity, sensitivity, and accuracy, play an important role in grading gliomas. Training future medical professionals in the effective use of AI is essential for its integration into clinical practice in the coming years. |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| Role of Artificial Intelligence.pdf | 1403/11/02 | 1269077 | دانلود |
| Certificate.jpg | 1403/11/02 | 518176 | دانلود |
| Scopus index.JPG | 1403/11/02 | 55895 | دانلود |
| Scopus index 2.JPG | 1403/11/02 | 77816 | دانلود |
