shivamsaxena07082000 / covid-19-classfication-using-xray-images

COVID-19 classfication

Automated Detection of COVID-19 Cases Using Deep Neural Networks with X-Ray Images

In this study, a deep learning model is proposed for the automatic diagnosis of COVID-19. The proposed model is developed to provide accurate diagnostics for multi-class classification (COVID vs. No-Findings vs. Pneumonia). Our model produced an average classification accuracy of 87.02% for multi-class cases when train for 100 iteration.

X-ray images obtained from two different sources were used for the diagnosis of COVID-19. A COVID-19 X-ray image database was developed by Cohen JP using images from various open access sources. This database is constantly updated with images shared by researchers from different regions.

Also, the ChestX-ray8 database provided by Wang et al. was used for normal and pneumonia images. In order to avoid the unbalanced data problem, we used 500 no-findings and 500 pneumonia class frontal chest X-ray images randomly from this database.

before running the app

pip install -r requirements.txt

to increase accuracy of prediction

• run the covid_classifier.ipynb and change the epoch number learn.fit_one_cycle(5, max_lr=3e-3) to learn.fit_one_cycle(100, max_lr=3e-3)

• put the model pickle file into covid classifier model folder

run app

streamlit run app.py

usage

• upload xray image of patient
• select classify image option
• click on classify x-ray image button