a hybrid deep transfer learning-based approach for covid-19 classification in chest x-ray images

The covid-19 pandemic is a severe public health hazard. hence, proper and early diagnosis is necessary to control the infection progression. we can diagnose this disease by employing a chest x-ray (cxr) screening, which is ordinarily cheaper and less harmful than a computed tomography scan (ct scan) and is continuously accessible in small or rustic hospitals. since the covid-19 dataset is inadequate and cannot be strictly distinguished from cxr, deep transfer learning (dtl) models can be used to diagnose coronavirus even with access to a small number of images. in this paper, we presented an approach to diagnosis covid-19 using cxr images based on the concatenated features vector of the three dtl structures and soft-voting feature selection procedure, including receiver of curve (roc), entropy, and signal-to-noise ratio (snr) techniques. our hybrid model reduces the feature vector size and classifies it in optimize manner to improve the decision-making process. a collection of 2,863 cxr images comprising normal, bacterial, viral, and covid-19 cases were prepared in jpeg format from the medical imaging center of vasei hospital, sabzevar, iran. the proposed approach obtained an accuracy of 99.34%, sensitivity of 99.48%, specificity of 99.27% while having a far fewer number of trainable parameters in contrast to its counterparts. compared to the latest similar methods, the diagnosis accuracy has increased from 1.5 to 2.2%. the comparative experiment reveals the advantage of the suggested covid-19 classification pattern based on dtl over other competing schemes.