A Deep Incremental Learning Framework for Predicting Covid-19 by using Incoming Stream X-ray Images of Chest

The COVID-19 epidemic has erupted in more than 150 nations around the world. One of the quickest ways to diagnose patients is to use radiography and radiology images to detect this disease. As the disease has not yet been eradicated, the number of these images is increasing daily and the dataset is constantly growing. In our framework, Covid -Stream, Images are entered into the framework as a stream of data. The proposed framework consists of two main parts. in, transfer learning phase features are extracted from these batch images using Keras library. Then incremental learning is applied to predict and evaluate COVID and non-COVID images using Creme library. Incremental learning plays an important role in this framework because it is not possible to process and fit all data into the memory. The proposed framework is tasted on a public CXR dataset (named COVID X-ray-5k) containing different chest abnormalities, and the proposed method achieved an accuracy of 0.86%. It also achieved a highly competitive performance while significantly reducing the training and computational burden. The proposed framework can solve real-world big datasets scalability issues.

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@inproceedings{undefined,
    author = {Sadeghi-Nasab, Alireza and Shakoor, Mohammad Hossein},
    title = {A Deep Incremental Learning Framework for Predicting Covid-19 by using Incoming Stream X-ray Images of Chest},
    booktitle = {7th International Conference on Technology Development in Iranian Electrical Engineering,
    publisher = {undefined},
    year = {1401},
    url = {https://civilica.com/doc/1492958},
}