CNN-Implementation
CNN-Implementation copied to clipboard
joyalshaji135
Automated detection of COVID-19 in real time can greatly help clinicians to handle increasing number of cases for preliminary screening. Deep CNN models trained with sufficiently large datasets may be...
CNN-Implementation
A total of 15,153 samples are used in this work. These samples include chest X-ray images of COVID-19, viral pneumonia, and normal cases. The entire dataset was split into train and test sets, with a ratio of 80:20 before training the model. To enhance important image features, image preprocessing and augmentation were applied before feeding the image batches to the model.
| Testing Result | |
|---|---|
| Test Implementation Name | Test Accuracy |
| CNN Implementation - 1 | 0.8780487775802612 | CNN Implementation - 2 | 0.9451219439506531 |
| Transfer_Learning_Implementation - 3 | 0.957317054271698 |
|
|
Metadata
Owner
joyalshaji135
Metadata
Automated detection of COVID-19 in real time can greatly help clinicians to handle increasing number of cases for preliminary screening. Deep CNN models trained with sufficiently large datasets may be...