Efficient generative transfer learning framework for the detection of COVID-19

Abstract

Deep learning plays a major role in detecting the presence of Coronavirus 2019 (COVID-19) and demands huge data. Availability of annotated data is a hurdle in using Deep learning technique. To enhance the accuracy of detection Deep Convolutional Generative Adversarial Network (DCGAN) is used to generate synthetic data. Densenet-201 is identified as the deep learning framework to detect COVID-19 from X-ray images. In this research, to validate the effectiveness of the Densenet-201, we explored conventional machine learning approaches such as SVM, Random Forest and Convolutional Neural Network (CNN). The feature map for training the machine learning approaches are extracted using Densenet-201 as feature extractor. The results show that Densenet-201 as feature representation with SVM is performing well in detecting COVID-19 with high accuracy. Moreover we experimented the proposed methodology without using DCGAN as well. DenseNet-201 based approach is capable of detecting the presence of COVID-19 with high accuracy. Experiments demonstrated that the proposed transfer learning approach based on DenseNet-201 along with DCGAN based augmentation outperforms the State of the art approaches like ResNet50, CNN, and VGG-16.