1. COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques.
- Author
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Mathesul, Shubham, Swain, Debabrata, Satapathy, Santosh Kumar, Rambhad, Ayush, Acharya, Biswaranjan, Gerogiannis, Vassilis C., and Kanavos, Andreas
- Subjects
X-rays ,DEEP learning ,X-ray imaging ,REVERSE transcriptase polymerase chain reaction ,MEDICAL personnel ,COVID-19 ,CONVOLUTIONAL neural networks - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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