Automated detection of Covid-19 disease using deep fused features from chest radiography images. (August 2021) - Uçar, Emine; Atila, Ümit; Uçar, Murat; Akyol, Kemal
Highlights: Direct detection of Covid-19 disease on chest radiography images. Realizing 3-class classification without the need for handcrafted features. Deep fused features from X-Ray images in different color spaces were used. An enhanced two-stage model was proposed for Covid-19 detection. Abstract: The health systems of many countries are desperate in the face of Covid-19, which has become a pandemic worldwide and caused the death of hundreds of thousands of people. In order to keep Covid-19, which has a very high propagation rate, under control, it is necessary to develop faster, low-cost and highly accurate methods, rather than a costly Polymerase Chain Reaction test that can yield results in a few hours. In this study, a deep learning-based approach that can detect Covid-19 quickly and with high accuracy on X-ray images, which are common in every hospital and can be obtained at low cost, was proposed. Deep features were extracted from X-Ray images in RGB, CIE Lab and RGB CIE color spaces using DenseNet121 and EfficientNet B0 pre-trained deep learning architectures and then obtained features were fed into a two-stage classifier approach. Each of the classifiers in the proposed approach performed binary classification. In the first stage, healthy and infected samples were separated, and in the second stage, infected samples were detected as Covid-19 or pneumonia. In the experiments, Bi-LSTM network and well-known ensemble approaches such as Gradient Boosting, Random Forest and Extreme Gradient Boosting were used as the classifier model and it was seen that the Bi-LSTM network had a superior performance than other classifiers with 92.489% accuracy.