Rapid detection of COVID-19 from chest X-ray images using deep convolutional neural networks

Panigrahi, Sweta; Raju, U. S. N.; Pathak, Debanjan; Kadambari, K. V.; Ala, Harika

doi:10.1504/ijbet.2023.128510

Cited by 1 publication

(1 citation statement)

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“…These studies successfully adapted pre-trained models from non-medical domains to medical datasets, demonstrating the effectiveness of this approach in rapidly deploying AI solutions for emerging health crises like COVID-19. Further extending the capabilities of CNNs, studies [25] and [26] focused on grading the severity of lung infections. This approach went beyond mere detection and provided critical insights into the extent of lung involvement, which is crucial for treatment planning in COVID-19 cases.…”

Section: Related Workmentioning

confidence: 99%

2D-CNN Architecture for Accurate Classification of COVID-19 Related Pneumonia on X-Ray Images

Dzhaynakbaev,

Kurmanbekkyzy,

Baimakhanova

et al. 2024

IJACSA

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In the wake of the COVID-19 pandemic, the use of medical imaging, particularly X-ray radiography, has become integral to the rapid and accurate diagnosis of pneumonia induced by the virus. This research paper introduces a novel twodimensional Convolutional Neural Network (2D-CNN) architecture specifically tailored for the classification of COVID-19 related pneumonia in X-ray images. Leveraging the advancements in deep learning, our model is designed to distinguish between viral pneumonia, typical of COVID-19, and other types of pneumonia, as well as healthy lung imagery. The architecture of the proposed 2D-CNN is characterized by its depth and a unique layer arrangement, which optimizes feature extraction from X-ray images, thus enhancing the model's diagnostic precision. We trained our model using a substantial dataset comprising thousands of annotated X-ray images, including those of patients diagnosed with COVID-19, patients with other pneumonia types, and individuals with no lung infection. This dataset enabled the model to learn a wide range of radiographic features associated with different lung conditions. Our model demonstrated exceptional performance, achieving high accuracy, sensitivity, and specificity in preliminary tests.The results indicate that our 2D-CNN model not only outperforms existing pneumonia classification models but also provides a valuable tool for healthcare professionals in the early detection and differentiation of COVID-19 related pneumonia. This capability is crucial for prompt and appropriate treatment, potentially reducing the pandemic's burden on healthcare systems. Furthermore, the model's design allows for easy integration into existing medical imaging workflows, offering a practical and efficient solution for frontline medical facilities. Our research contributes to the ongoing efforts to combat COVID-19 by enhancing diagnostic procedures through the application of artificial intelligence in medical imaging.

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Section: Related Workmentioning

confidence: 99%