/Corona-Detection-from-X-ray-using-CNN

The first 6 layers of convolution network are convolution layer. First 2 convolution layer applies 16 of 33 filters to an image in the layer. The other two layer applies 32 of 33 filters to an image.

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Corona Detection from X-ray using CNN

STEPS TO BUILD DETECTION MODEL corona detection

Corona virus disease (COVID-19) is an infectious disease caused by a newly discovered corona virus. 465,915 Confirmed cases & 21,031 Confirmed deaths (Updated : 27 March 2020 ) , corona has spread in m ore than 200 countries.

Corona virus caused cluster of pneumonia cases, in late 2019, majority suffering from only mild, cold-like symptoms. According to researchers, the lining of the respiratory tree becomes injured, causing inflammation. This in turn irritates the nerves in the lining of the airway. Just a speck of dust can stimulate a cough. If the condition becomes intense, lungs that become filled with inflammatory material become unable to get enough oxygen to the bloodstream, reducing the body’s ability to take on oxygen and get rid of carbon dioxide which finally lead to death.

  1. X-ray images for patients who have tested positive for COVID-19 are collected
  2. “normal” (i.e., not infected) X-ray images from healthy patients are collected
  3. Divide the dataset in test, train and validate dataset
  4. Train a CNN to automatically detect COVID-19 in X-ray images via the dataset we created
  5. Evaluate the results

corona detection output

Dataset link : https://bit.ly/3dvXVa0

visit https://aihubprojects.com/corona-detection-from-x-ray-using-cnn/