Enhanced Deep Learning Based X-Ray Analysis for COVID-19 Identification

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ID: 312756
2025
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Abstract
The rapid and accurate detection of COVID-19 is critical for mitigating its transmission and ensuring timely medical intervention. This research enhances COVID-19 detection by implementing the Artificial Neural Networks Algorithms. Our research paper embeds the concept of a Convolutional Neural Network for efficient and accurate detection of COVID-19 by taking x-rayed images of the lungs of patients as input. The proposed model development involves systematic steps, including data acquisition, preprocessing, and augmentation, as well as the application of a convolutional neural network to the prepared data. The dataset utilized for this research paper on COVID-19 detection using Artificial Neural Network (ANN) is obtained from the source of the website Kaggle. The dataset consists of three classes: normal, viral, and COVID-19 affected. The visual data of these three classes is utilized to train and test the model. PCR testing is the most used technique for COVID-19 detection, but this technique is pricey for people who belong to middle- or lower-class families, so our research paper overcomes this financial barrier by using X-ray images of the patient to detect whether the patient is infected with COVID-19 or not. Accurate identification of COVID-19 cases is vital for controlling its transmission. Minimizing false negatives ensures timely care for infected individuals, reducing spread. Our proposed model achieves an accuracy of 95% by using multiple layer Convolutional Neural Network.
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Authors Noor-ul-Ain Yousaf
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/801
Keywords Keywords not found

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