ABSTRACT
The Covid-19 spun into a pandemic and has affected routine lives and global health. It is crucial to identify the infectious Covid-19 subjects as early as possible to avert its spread. The CXR images processed with deep learning (DL) processes have newly become an earnest method for early Covid-19 detection along with the regular RT-PCR test. This paper examines the deep learning (DL) models to detect Covid-19 from CXR images for early analysis of Covid-19. We conducted an empirical study to assess the efficacy of the proposed convolutional neural network DL model (CNN-DLM), pre-trained with some eminent networks such as MobileNet, InceptionNet-V3, ResNet50, Xception, and DenseNet121 for initial detection of Covid-19 for an openly accessible dataset. We also exhibited the accuracy and loss value curves for the selected number of epochs for all these models. The results indicate that with the proposed CNN model pre-trained with the DenseNet121 greater results were achieved compared to other pre-trained CNN-DLMs applied in a transfer learning approach. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.