The Project is a model that is based on Deep Learning which is trained on some data sets to provide an accuracy above 95%, we have created 3 scenarios that take some dataset on some ratios and provide the accuracy. The result is shown in binary i.e 0 or 1 for Covid-19 or Normal.&nb
Classification of Covid and Pneumonia using Deep Learning
The Project is a model that is based on Deep Learning which is trained on some data sets to provide an accuracy above 95%, we have created 3 scenarios that take some dataset on some ratios and provide the accuracy. The result is shown in binary i.e 0 or 1 for Covid-19 or Normal. We have also presented some charts for graphical representation at the end of the model.
The objective is for the practicing doctors so that they can identify which chest x-ray is of Covid-19 or Normal. Since the pandemic started many patients were admitted by mistakes of the practicing doctors so to avoid that we created the model which is trained on the datasets to identify for them.
Implementation method is simple why ? because we wanted to make the model in a simple manner so even a non technical person can understand what is happening in it the steps are as follows :
1- Created senerios on Jupyter Notebook.
2- Uploaded them on dropbox so we can easily use them in our Colabs notebook.
3- Imported the libraries we need for the model in colabs.
4- Trained the model in google colabs so that even if we need the extra resources we could buy them.
Benefits :
1- Easy to use for the practicing docters since they just have to use the x-ray name.
2- provides an accuracy above 95% in medical field thats appropriate for a model.
3- we made it in such a way that it can be more advanced by adding more layers and images to the dataset
4- workload would be reduced in testing the patients.
5- the model is not just for Covid-19 it can be used in other diseases too.
There is no such rocket science in the technical details of our project since we wanted to make it simple in the pandemic for all users .
How its working ->
Simply the datasets are being called from the dropbox and imported in the notebooks.
Then the model trains and creates 2 class indices 0 & 1.
Then we can provide pictures to it and get results based on binary numbers.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| google colab pro | Miscellaneous | 1 | 1817 | 1817 |
| Total in (Rs) | 1817 |
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