Papers › Covid-19 chest x-ray image generation using resnet-dcgan model
Covid-19 chest x-ray image generation using resnet-dcgan model
Sukonya Phukan, Jyoti Singh, Rajlakshmi Gogoi, Sandipan Dhar, and Nanda Dulal Jana
Detection of severe diseases like COVID-19 using deep learning (DL) models is a very time-relevant subject to be focused on looking at the present scenario. However, there is always a problem regarding the availability of significant data for the training of DL-based classification models. In this work, a ResNet-DCGAN model is proposed to generate synthesized COVID-19 chest X-ray images to tackle the data scarcity problem. Here, some image processing techniques are applied to the publicly available COVID-19 chest X-ray images. Thereafter, a ResNet50 Deep Convolutional Neural Network (DCNN) model is incorporated as the discriminator to the proposed ResNet-DCGAN model. Moreover, in this work, to train the proposed model efficiently, the RAdam optimization algorithm is used instead of the earlier Adam optimization algorithm. The proposed ResNet-DCGAN model had the edge over the state-of-the-art DCGAN model.
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