این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
International Journal of Nonlinear Analysis and Applications، جلد ۱۳، شماره ۱، صفحات ۱۳۵۱-۱۳۶۵
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Detection of COVID-19 from radiology modalities and identification of prognosis patterns
چکیده انگلیسی مقاله SARS-CoV-2 and the consequential COVID-19 virus is one of the major concerns of the 21st century. Pertaining to the novelty of the disease, it became necessary to discover the efficacy of deep learning techniques in the quick and consistent discovery of COVID-19 based on chest X-ray and CT scan image analysis. In this related work, Prognostic tool using regression was designed for patients with COVID-19 and recognizing prediction patterns to make available important prognostic information on mortality or severity in COVID-19 patients. And reliable convolutional neural network (CNN) architecture models (DenseNet, VGG16, ResNet, Inception Net)to institute whether it would work preeminent in terms of accuracy as well as efficiency with image datasets with Transfer Learning. CNN with Transfer Learning were functional to accomplish the involuntary recognition of COVID-19 from numerary chest X-ray and CT scan images. The experimental results emphasize that selected models, which is formerly broadly tuned through suitable parameters, executes in extensive levels of COVID-19 discovery against pneumonia or normal or lung opacity through the precision of up to 87% for X-Ray and 91% intended for CT scans.
کلیدواژه‌های انگلیسی مقاله Convolutional Neural Network, Transfer learning, COVID-19, X-ray, CTscan, Deep learning
نویسندگان مقاله G JayaLakshmi |
Department of IT, V R Siddhartha Engineering College, India

Haitham Abbas Khalaf |
College of Medicine, University of Anbar, Ramadi, Iraq

Abolfazl Farhadi |
Department of Nursing Sciences, Shirvan Branch, Islamic Azad University, Shirvan, Iran

Shokhan M Al Barzinji |
Department of Computer Science, College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq

Sawsan Dheyaa Mahmood |
Department of Electricity Engineering College of Engineering, University of Tikrit, Tikrit, Iraq

Saif Al-din M Najim |
Department of Computer Science, College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq

Maha A Hutaihit |
Department of Communication Engineering Collage of Engineering, University of Diyala: Baqubah, Diyala, Iraq

Salwa Mohammed Nejrs |
Directorate of Private University Education, Ministry of Higher Education, Baghdad, Iraq

Raghda Salam Al Mahdawi |
Department of Computer Engineering Collage of Engineering, University of Diyala: Baqubah, Diyala, Iraq

Azmi Shawkat Abdulbaqi |
Department of Computer Science, College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq

نشانی اینترنتی https://ijnaa.semnan.ac.ir/article_5713_7701a9b6423576ef1118f15f349f8d44.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات