# COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques
###### Abstract  
The COVID-19 pandemic has posed significant challenges in accurately diagnosing the disease, as severe cases may present symptoms similar to pneumonia. Real-Time Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) is the conventional diagnostic technique; however, it has limitations in terms of time-consuming laboratory procedures and kit availability. Radiological chest images, such as X-rays and Computed Tomography (CT) scans, have been essential in aiding the diagnosis process. In this research paper, we propose a deep learning (DL) approach based on Convolutional Neural Networks (CNNs) to enhance the detection of COVID-19 and its variants from chest X-ray images. Building upon the existing research in SARS and COVID-19 identification using AI and machine learning techniques, our DL model aims to extract the most significant features from the X-ray scans of affected individuals. By employing an explanatory CNN-based technique, we achieved a promising accuracy of up to 97% in detecting COVID-19 cases, which can assist physicians in effectively screening and identifying probable COVID-19 patients. This study highlights the potential of DL in medical imaging, specifically in detecting COVID-19 from radiological images. The improved accuracy of our model demonstrates its efficacy in aiding healthcare professionals and mitigating the spread of the disease.  
COVID-19 detection; X-ray images; Canny edge detector; Grad-CAM; deep learning Article COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques  
[PERSON], 1, [PERSON], 2  
(CNNs), have demonstrated remarkable capabilities in image analysis tasks and present a promising avenue for automated COVID-19 detection using chest X-ray scans [6].  
In this research, we propose a novel DL model for automated COVID-19 detection from chest X-ray images [7]. Our model aims to perform extensive binary diagnostics, distinguishing COVID-19 findings from non-COVID-19 findings. To train the model, we utilize a large dataset of publicly accessible chest radiograph images, which includes clinical results consistent with COVID-19, along with verified cases of COVID-19 for creating the test dataset [8]. By leveraging the power of CNN architecture, our DL model can effectively learn to detect abnormalities in chest X-ray scans and aid healthcare professionals in triaging high-risk patients, especially in inpatient settings.  
This paper serves as a comprehensive exploration of cutting-edge deep learning techniques utilized for the detection of COVID-19 from chest X-ray images. It not only showcases the power of this technology in enhancing diagnostic speed and precision but also underscores its broader impact on healthcare systems. By accelerating the identification of COVID-19 cases, these techniques play a pivotal role in optimizing resource allocation, enabling faster isolation and treatment of affected individuals, and ultimately reducing the risk of transmission. Through this research, we aim to contribute to the growing body of knowledge that leverages AI-driven solutions to combat the ongoing pandemic, thus fostering more resilient healthcare systems for the future.  
The structure of the paper is as follows: Section 2 provides an overview of related research in the field and highlights the unique contributions of our work. Section 3 presents the detailed processing steps of our proposed approach. In Section 4, we present the experimental findings and performance comparisons. Finally, Section 5 concludes the paper with a summary of our research findings and outlines potential future research directions.