期刊
ALGORITHMS
卷 16, 期 10, 页码 -出版社
MDPI
DOI: 10.3390/a16100494
关键词
COVID-19 detection; X-ray images; Canny edge detector; Grad-CAM; deep learning
In this research paper, a deep learning approach based on Convolutional Neural Networks (CNNs) is proposed to enhance the detection of COVID-19 from chest X-ray images. By extracting the most significant features from the X-ray scans, the model achieved a promising accuracy of up to 97% in detecting COVID-19 cases, aiding physicians in effectively screening and identifying probable patients.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据