Abstract
The COVID-19 has infected around 340 million people, and 5.5 million died. Due to the rapid growth of the virus, and limited resources, the healthcare sector collapsed in many countries. Hence, there is a need to study deep learning- based applications that can aid the healthcare sector. The primitive machine learning approaches require learned features to extract information for classification, whereas Convolutional Neural Network (CNN) performs the same by extracting image features from raw images. CNN tends to overfit often for small datasets, and hence, the concept of transfer learning comes into play. This paper aims to study and modify a pre-trained CNN VGG-16 model using the concept of transfer learning. The algorithm has been validated using a private and a public dataset with normal and COVID-19 positive chest X-ray images.
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