Recognition of COVID-19 Disease Utilizing X-Ray Imaging of the Chest Using CNN

TL;DR

This study evaluates CNN models with varying layers for COVID-19 detection using chest X-ray images, demonstrating that a three-layer CNN achieves 96% accuracy, supporting its use for reliable screening.

Contribution

The paper introduces a CNN architecture with three convolution layers optimized for COVID-19 detection from chest X-rays, showing high accuracy in experimental tests.

Findings

Three-layer CNN achieves 96% accuracy

Model reliably detects COVID-19 in X-ray images

Deep learning enhances screening efficiency

Abstract

Since this COVID-19 pandemic thrives, the utilization of X-Ray images of the Chest (CXR) as a complementary screening technique to RT-PCR testing grows to its clinical use for respiratory complaints. Many new deep learning approaches have developed as a consequence. The goal of this research is to assess the convolutional neural networks (CNNs) to diagnosis COVID-19 utisizing X-ray images of chest. The performance of CNN with one, three, and four convolution layers has been evaluated in this research. A dataset of 13,808 CXR photographs are used in this research. When evaluated on X-ray images with three splits of the dataset, our preliminary experimental results show that the CNN model with three convolution layers can reliably detect with 96 percent accuracy (precision being 96 percent). This fact indicates the commitment of our suggested model for reliable screening of COVID-19.