ArXiv Preprint
In medical practice, the contribution of information technology can be
considerable. Most of these practices include the images that medical
assistance uses to identify different pathologies of the human body. One of
them is X-ray images which cover much of our work in this paper. Chest x-rays
have played an important role in Covid 19 identification and diagnosis. The
Covid 19 virus has been declared a global pandemic since 2020 after the first
case found in Wuhan China in December 2019. Our goal in this project is to be
able to classify different chest X-ray images containing Covid 19, viral
pneumonia, lung opacity and normal images. We used CNN architecture and
different pre-trained models. The best result is obtained by the use of the
ResNet 18 architecture with 94.1% accuracy. We also note that The GPU execution
time is optimal in the case of AlexNet but what requires our attention is that
the pretrained models converge much faster than the CNN. The time saving is
very considerable. With these results not only will solve the diagnosis time
for patients, but will provide an interesting tool for practitioners, thus
helping them in times of strong pandemic in particular.
Benbakreti Samir, Said Mwanahija, Benbakreti Soumia, Umut Özkaya
2023-01-06
