Advance Warning Methodologies for COVID 19 using Chest X Ray Images

Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019 . As a result, accurate and reliable advance warning system for the early diagnosis has now become a priority . Both compact classifiers and deep learning approaches are considered in this study . A detailed set of experiments shows that the CSEN achieves the top (over 98.5%) sensitivity with over 96% specificity . Transfer learning over the deep CheXNet fine-tuned with the augmented data produces the leading performance among other deep networks with 97.14% sensitivity and 99.49% specificity. Moreover, transfer learning over . the . deep . networks produces the . leading performance in the . augmented data produced by the augmented . data produces a new benchmark dataset called Early-QaTa-COV19, which consists of 175 early-stage COVE-19 Pneumonia samples (very limited or no infection signs) labelled by the medical doctors and 1579 samples for control (normal) class . The study introduces a benchmark dataset to the new benchmark datasets called Early QaTa

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Keywords : deep - early - learning - produces - data -

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