Industry 4.0 technologies and their applications in fighting COVID-19 pandemic using deep learning techniques

Area of Science:

• Medical Imaging
• Artificial Intelligence in Healthcare
• Infectious Disease Diagnostics

Background:

• The COVID-19 pandemic necessitates rapid and accurate diagnostic tools.
• Industry 4.0 technologies, including AI, offer solutions for public health crises.
• Clinical imaging, particularly X-rays, is crucial for early COVID-19 detection.

Purpose of the Study:

• To develop and evaluate a deep convolutional neural network (CNN) for automated COVID-19 identification from X-ray images.
• To compare the proposed CNN model's performance against established models like VGG16, AlexNet, and InceptionV3.
• To create an automated screening system for COVID-19 patients with reduced human interaction.

Main Methods:

• Utilized deep convolutional neural networks (CNNs) for feature extraction and image discrimination.
• Applied image processing techniques to isolate the region of interest (ROI) in X-ray images.
• Employed data augmentation using the ImageDataGenerator class to expand a small dataset.

Main Results:

• The proposed CNN model demonstrated superior performance compared to VGG16, AlexNet, and InceptionV3.
• Achieved high accuracy rates: 97.68% for two-class classification, 89.85% for three classes, and 84.76% for four classes.
• The system facilitates automated screening of COVID-19 patients.

Conclusions:

• Deep CNNs are effective for automated COVID-19 detection using chest X-rays.
• The developed system offers a rapid and accurate screening solution for the pandemic.
• AI-powered medical imaging can significantly aid public health responses during emergencies.