Abstract
The use of artificial intelligence (AI) and convolutional neural networks (CNNs) in medicine has enabled the analysis and categorisation of X-ray images for various diagnoses. This study proposes a deep learning architecture for the diagnosis of COVID-19 by categorising chest X-ray images. However, the limited availability of a large and high-quality chest X-ray image dataset posed a challenge in developing a reliable and accurate CNN classifier. To address this, the dataset underwent multiple pre-processing stages, including dataset balancing, picture analysis by medical professionals, and data augmentation, to achieve the best performance. The proposed CNN model achieved an overall accuracy of 90.27%, with 91.06% precision, 82.40% recall, and 85.61% FSCORE, with only 9.72% incorrect predictions when validated with a different set of COVID-19 X-ray images. Furthermore, a comparison with other machine learning techniques demonstrated that the suggested model outperformed existing models, including KNN, Naive Bayes, LSTM, SVM, ResNet, InceptionV3, Decision Tree, Logistic Regression, and MobileNet, when evaluated against an independent testing set. Therefore, this study presents an efficient approach for COVID-19 disease detection on X-ray images using a CNN model.
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Keshamoni, K., Koteswara Rao, L., Subba Rao, D. (2023). An Efficient COVID-19-Based Disease Detection on X-Ray Images Using CNN Model. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Infrastructure and Computing. ICT4SD 2023. Lecture Notes in Networks and Systems, vol 754. Springer, Singapore. https://doi.org/10.1007/978-981-99-4932-8_33
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DOI: https://doi.org/10.1007/978-981-99-4932-8_33
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