Author/Authors :
JayaLakshmi, G Department of IT - V R Siddhartha Engineering College, India , Abbas Khalaf, Haitham College of Medicine - University of Anbar, Ramadi, Iraq , Farhadi, Abolfazl Department of Nursing Sciences - Islamic Azad University Shirvan Branch, Shirvan, Iran , Al Barzinji, Shokhan M Department of Computer Science - College of Computer Science and Information Technology - University of Anbar, Ramadi, Iraq , dheyaa Mahmood, Sawsan Department of Electricity Engineering College of Engineering - University of Tikrit, Tikrit, Iraq , Al-din M Najim, Saif Department of Computer Science - College of Computer Science and Information Technology - University of Anbar, Ramadi, Iraq , Hutaihit, Maha A Department of Communication Engineering - Collage of Engineering - University of Diyala, Baqubah, Diyala, Iraq , Mohammed Nejrs, Salwa Directorate of Private University Education -Ministry of Higher Education, Baghdad, Iraq , Al Mahdawi, Raghda Salam Department of Computer Engineering - Collage of Engineering -University of Diyala, Baqubah, Diyala, Iraq , Shawkat Abdulbaqi, Azmi Department of Computer Science - College of Computer Science and Information Technology - University of Anbar, Ramadi, Iraq
Abstract :
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.
Keywords :
convolutional neural network , transfer learning , COVID-19 , X-ray , CTscan , deep learning
