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Comparing CT scan and chest X-ray imaging for COVID-19 diagnosis
Elmehdi Benmalek1, Jamal Elmhamdi1, Abdelilah Jilbab1
1Laboratory LRGE, ENSET, Mohammed V University, Rabat, Morocco.
Biomedical Engineering Advances
|November 17, 2021
Summary
Rapid COVID-19 diagnosis is crucial for patient care and public health. This study shows that artificial intelligence models using chest CT scans can accurately detect the virus, offering a faster alternative to traditional lab tests.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Infectious Diseases
Background:
- Timely diagnosis of COVID-19 is essential for effective patient management and disease control.
- Current laboratory testing (RT-PCR) is time-consuming and requires specialized facilities.
- Chest imaging, including CT scans and X-rays, shows promise for rapid COVID-19 detection.
Purpose of the Study:
- To compare the diagnostic performance of different convolutional neural networks (CNNs) for COVID-19 detection using chest imaging.
- To evaluate the effectiveness of Resnet-18, InceptionV3, and MobileNetV2 models on CT and CXR images.
Main Methods:
- Utilized chest X-ray (CXR) and computed tomography (CT) scan images for analysis.
- Applied three distinct CNN models: Resnet-18, InceptionV3, and MobileNetV2.
- Assessed model performance based on precision, sensitivity, and specificity.
Main Results:
- ResNet-18 achieved high precision (98.5%) and sensitivity (98.6%).
- InceptionV3 demonstrated excellent specificity (97.4%).
- MobileNetV2 exhibited perfect sensitivity for COVID-19 cases, all with CT scan images.
Conclusions:
- Convolutional neural networks, particularly ResNet-18, show high accuracy in diagnosing COVID-19 from CT scans.
- AI-powered chest imaging offers a rapid and reliable alternative for COVID-19 diagnosis.
- These findings support the integration of AI in medical imaging for infectious disease detection.
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