Deep learning in medical imaging has revolutionized the way we interpret medical data, as high computational devices' capabilities are far more than their creators. With the pandemic causing havoc for the second straight year, the findings in our paper will allow researchers worldwide to use and create state-of-the-art models to detect affected persons before it reaches the R number. The paper proposes an automated diagnostic tool using the deep learning models on chest x-rays as an input to reach a point where we surpass this pandemic (COVID-19 disease). A deep transfer learning-based model for automatic detection of COVID-19 from chest x-rays using the Inception-V3 model is proposed, in which we added flattening, node dropping, normalization, and dense layer. The proposed architecture is compared with existing state-of-the-art ImageNet models. The model?s efficacy is tested on three different COVID-19 radiography datasets with three classes: COVID, normal, and viral pneumonia. The proposed model has reached an accuracy of 97.7%, 84.95%, and 97.03% on the mentioned datasets, respectively. The proposed work introduces the deep neural networks applied to medical images to analyze image enhancement techniques and emphasize the field's clinical aspects.
COVID-19, chest X-ray, computer vision, digital ımage processing, transfer learning
CHHIKARA, PRATEEK; GUPTA, PRAKHAR; SINGH, PRABHJOT; and BHATIA, TARUNPREET
"A deep transfer learning based model for automatic detection of COVID-19from chest X-rays,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 29:
8, Article 6.
Available at: https://journals.tubitak.gov.tr/elektrik/vol29/iss8/6