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Diagnotica X Application for detecting COVID-19 in chest X-ray images

Author: Jesus Ramseths Echeverria

  • This project is designed for the Sigmahacks 3 hackathon.
  • You need to have Python 3.7 or higher and PyQt5.

Stage 1:

First, a Jupyter Notebook is created with the algorithm to train the pre-trained model with ImageNet DenseNet-201. You can view it in the model/train_model folder. It was trained with images from an external public access repository.

Stage 2:

The interface is created in Qt Creator to achieve a more comfortable interaction for the user, in addition to maintaining the KISS principle.

Stage 3:

The model is saved in the model folder and is used in the app.py script, in which the whole PyQt5 structure is implemented.

Stage 4:

Finally, it is tested with two internet images, one of the chest with COVID-19 and the other of the normal chest, both of which are X-ray images, in addition to being images never seen before by our model, which successfully predicted both images.

About DenseNet-201

It is a powerful 201-layer convolutional neural network that obtains a more minimal error compared to the human eye. This network was trained with ImageNet, so only in this project we did transfer learning for our two classes [Normal, Covid19].