article · Diagnostics
Chest radiographs, computed tomography scans, and electrocardiogram trace images are widely recognised imaging modalities used to detect COVID-19. Whereas previous deep learning models generally rely on a single modality, evaluating several data types can uncover abnormal patterns associated with the virus and provide more comprehensive diagnostic information. A deep learning framework based on the ShuffleNet convolutional neural network was tested across all three image formats at varying classification granularities. Using chest radiographs, the model achieved an average accuracy of 99.98% in three-class classification, outperforming contemporary architectures such as SqueezeNet, AlexNet, and DarkNet19. On CT scans, it achieved 100% accuracy in binary classification between normal and infected cases. Finally, evaluations on ECG trace images produced an average accuracy of 99.37% across five diagnostic classes, marking an accuracy gain of 1.54% over prior approaches.
Rapid and accurate diagnosis is essential for limiting the transmission of COVID-19. By demonstrating that a single neural network architecture can accurately interpret different clinical modalities, including lung scans and cardiac traces, the research highlights how automated tools could assist healthcare providers in identifying infections reliably across varied standard diagnostic inputs.
The method could enable diagnostic decision-support software for clinicians and medical imaging departments seeking automated screening of COVID-19 from radiography, CT, or cardiac traces. The work is at an applied and tested stage, validated on benchmark datasets including the COVID-19 Radiography Database and SARS-CoV-2 CT collections, though the abstract does not report real-world clinical deployment or regulatory approval pathways.
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Early and precise COVID-19 identification and analysis are pivotal in reducing the spread of COVID-19. Medical imaging techniques, such as chest X-ray or chest radiographs, computed tomography (CT) scan, and electrocardiogram (ECG) trace images are the most widely known for early discovery and analysis of the coronavirus disease (COVID-19). Deep learning (DL) frameworks for identifying COVID-19 positive patients in the literature are limited to one data format, either ECG or chest radiograph images. Moreover, using several data types to recover abnormal patterns caused by COVID-19 could potentially provide more information and restrict the spread of the virus. This study presents an effective COVID-19 detection and classification approach using the Shufflenet CNN by employing three types of images, i.e., chest radiograph, CT-scan, and ECG-trace images. For this purpose, we performed extensive classification experiments with the proposed approach using each type of image. With the chest radiograph dataset, we performed three classification experiments at different levels of granularity, i.e., binary, three-class, and four-class classifications. In addition, we performed a binary classification experiment with the proposed approach by classifying CT-scan images into COVID-positive and normal. Finally, utilizing the ECG-trace images, we conducted three experiments at different levels of granularity, i.e., binary, three-class, and five-class classifications. We evaluated the proposed approach with the baseline COVID-19 Radiography Database, SARS-CoV-2 CT-scan, and ECG images dataset of cardiac and COVID-19 patients. The average accuracy of 99.98% for COVID-19 detection in the three-class classification scheme using chest radiographs, optimal accuracy of 100% for COVID-19 detection using CT scans, and average accuracy of 99.37% for five-class classification scheme using ECG trace images have proved the efficacy of our proposed method over the contemporary methods. The optimal accuracy of 100% for COVID-19 detection using CT scans and the accuracy gain of 1.54% (in the case of five-class classification using ECG trace images) from the previous approach, which utilized ECG images for the first time, has a major contribution to improving the COVID-19 prediction rate in early stages. Experimental findings demonstrate that the proposed framework outperforms contemporary models. For example, the proposed approach outperforms state-of-the-art DL approaches, such as Squeezenet, Alexnet, and Darknet19, by achieving the accuracy of 99.98 (proposed method), 98.29, 98.50, and 99.67, respectively.
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DOI: 10.3390/diagnostics13010162
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