COVID-19 or novel coronavirus disease, which has already been declared as a worldwide pandemic, at first had an outbreak in a small town of China, named Wuhan. More than two hundred countries around the world have already been affected by this severe virus as it spreads by human interaction. Moreover, the symptoms of novel coronavirus are quite similar to the general flu. Screening of infected patients is considered as a critical step in the fight against COVID-19. Therefore, it is highly relevant to recognize positive cases as early as possible to avoid further spreading of this epidemic. However, there are several methods to detect COVID-19 positive patients, which are typically performed based on respiratory samples and among them one of the critical approach which is treated as radiology imaging or X-Ray imaging. Recent findings from X-Ray imaging techniques suggest that such images contain relevant information about the SARS-CoV-2 virus.
Deep learning techniques showed in the last years promising results to accomplish radiological tasks by automatic analyzing multimodal medical images. In previous studies, DCNNs have been exploited in X-ray image classification to successfully diagnose common chest diseases such as Tuberculosis screening and mediastinal lymph nodes in CT images. However, the application of deep learning techniques to identify and detect novel COVID-19 in X-ray is still very limited so far. Therefore, in this project the aim is to propose a framework based of pre-trained deep learning classifiers as an advanced tool to assist radiologists to automatically diagnose COVID-19 in X-ray images. Due to shortage of proper X-ray samples, neural networks become difficult to train. Therefore to compensate this, the model is trained previously on datasets containing information about various chest related infections.
The COVID-19 pandemic has brought radiologists’ penchant for descriptive terms front-and-centre, with frequent references to one feature in particular: ground-glass opacities. The term refers to the hazy, white-flecked pattern seen on lung CT scans, indicative of increased density. Ground-glass opacities aren’t likely to be found in healthy lungs, though, and wouldn’t result from exposures like air pollution or smoking. There are a lot of diseases that can cause ground-glass opacities, but in COVID-19, there’s a distinct distribution, a preference for certain parts of the lung. COVID-related ground-glass opacities also have a very round shape that’s unusual compared with other ground-glass opacities.
Upload the scanned images of Chest X-Rays here and get the prediction instantly. Currently, CT images are not supported
You can use curl to test the API or any other API testing tool like POSTMAN
$ curl -i \
-X POST \
-H "Content-Type: multipart/form-data" \
-F "image=@<your_image_name>" \
HTTP/1.1 200 OK
Server: Werkzeug/0.14.1 Python/3.6.7
Date: Sun, 14 Nov 2021 17:44:32 GMT
"API version": "1",
"Response time": "0.3548761470010504 seconds",
"disclaimer": "This API does not claim any medical correctness for the rendered results",
"Message":"Image successfully received",
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