basharatwali / covid19-cnn-lstm Goto Github PK

Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An automatic disease detection framework assists doctors in the diagnosis of disease and provides exact, consistent, and fast results and reduces the death rate. Coronavirus (COVID-19) has become one of the most severe and acute diseases in recent times and has spread globally. Therefore, an automated detection system, as the fastest diagnostic option, should be implemented to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 4575 X-ray images, including 1525 images of COVID-19, were used as a dataset in this system. The experimental results show that our proposed system achieved an accuracy of 99.4%, AUC of 99.9%, specificity of 99.2%, sensitivity of 99.3%, and F1-score of 98.9%. The system achieved desired results on the currently available dataset, which can be further improved when more COVID-19 images become available. The proposed system can help doctors to diagnose and treat COVID-19 patients easily.

The dataset can be found in here. For dataset related queries, please drop an email to [email protected]

Please cite our paper if you find the work useful:

@article{islam2020combined,
  title={A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images},
  author={Islam, Md Zabirul and Islam, Md Milon and Asraf, Amanullah},
  journal={Informatics in medicine unlocked},
  volume={20},
  pages={100412},
  year={2020},
  publisher={Elsevier}
}

M. Z. Islam, M. M. Islam, and A. Asraf, ‘‘A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images,’’ Informat. Med. Unlocked, vol. 20, pp. 100412, Aug. 2020.

Paper: https://doi.org/10.1016/j.imu.2020.100412