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RETRACTED ARTICLE: COVID-19 Detection using adopted convolutional neural networks and high-performance computing.

Anil Kumar Singh¹, Ankit Kumar², Vinay Kumar³

¹Department of Computer Science and Engineering, Pranveer Singh Institute of Technology, Dr. APJ Abdul Kalam Technical University, Lucknow, India.

Multimedia Tools and Applications

|June 26, 2023

Summary

This summary is machine-generated.

This study introduces a freely-available artificial intelligence model using deep convolutional neural networks (CNN) with ResNet50 for detecting COVID-19 from chest X-rays. The AI model achieves approximately 97% accuracy in identifying coronavirus disease.

Keywords:

CNN Chest radiography pictures Computed tomography (CT) scan Covid-19 Polymerase chain response ResNet101 ResNet50

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Area of Science:

Medical Imaging
Artificial Intelligence
Infectious Diseases

Background:

The COVID-19 pandemic poses a significant global health threat, necessitating rapid and accurate diagnostic tools.
Radiological imaging, particularly chest radiography, is crucial for screening and assessing respiratory system involvement.

Purpose of the Study:

To develop and present an accessible artificial intelligence (AI) model for detecting COVID-19 from chest radiography scans.
To evaluate the model's performance in identifying coronavirus disease in real-time.

Main Methods:

A deep convolutional neural network (CNN) model with a ResNet50 configuration was employed.
The model was trained and validated using chest radiography images to detect features indicative of COVID-19.
A database was utilized for tracking detected patients to enhance model accuracy.

Main Results:

The proposed AI model demonstrated high accuracy, achieving approximately 97% in detecting COVID-19 from chest radiography scans.
The model is capable of recognizing coronavirus disease and assessing the real-time condition of patients.

Conclusions:

The developed AI model, based on CNN and ResNet50, offers a promising and accessible tool for COVID-19 detection using chest X-rays.
The model's accuracy and database integration support its potential for widespread clinical application and improved diagnostic efficiency.