Title:COVID-19 Detection using Hybrid CNN-RNN Architecture with Transfer
Learning from X-Rays
Volume: 20
Author(s): Deepti Deshwal, Pardeep Sangwan, Naveen Dahiya, Umesh Kumar Lilhore, Surjeet Dalal*Sarita Simaiya
Affiliation:
- Department of Computer Science and Engineering, Amity University Haryana, Gurugram, India
Keywords:
Transfer learning, Convolutional neural network, Data privacy, Security, Recurrent neural network, Deep learning.
Abstract:
Introduction:
Millions of people have been infected with COVID-19, which has spread quickly worldwide since the start of 2020, resulting in numerous
fatalities. Identification of infected individuals is essential to control the spread of the virus.
Aims:
In this study, we propose a hybrid architecture that combines Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs)
and leverages transfer learning to enhance the accuracy of COVID-19 detection from X-ray images.
Methods:
The proposed work utilizes 4 pre-trained CNN architectures, namely, InceptionnetV3, Densenet121, Inception-ResNet V2, and VGG19, to extract
high-level features from the input X-ray images. These features are then fed into the second component, an RNN-based network, which captures
the temporal dependencies within the extracted features. To evaluate the performance of the proposed architecture, a comprehensive dataset
consisting of X-ray images from COVID-19 positive cases, non-COVID-19 pneumonia cases, and healthy individuals is used. Gradient class
activation map (Grad-CAM) analysis has been applied to the obtained results to provide heat-map pictures specific to each class and coloured
visualizations of the COVID-19-infected areas in CXR images.
Results:
: Experimental results demonstrate that the proposed hybrid CNN-RNN architecture achieves promising results in COVID-19 detection from X-ray
images. The model exhibits high accuracy, precision, recall, area under the receiver operating characteristics (ROC) curve (AUC), and F1-score,
outperforming other state-of-the-art methods.
Conclusion:
The combination of CNNs and RNNs enables the model to effectively capture spatial and temporal information, leading to improved performance
in COVID-19 detection. The proposed hybrid architecture with transfer learning from X-ray images provides a robust and efficient solution for
COVID-19 detection. The model can potentially assist healthcare professionals in making accurate and timely diagnoses, thereby contributing to
the global efforts to combat the COVID-19 pandemic. In the present work, VGG19-RNN architecture outperformed all other networks in terms of
accuracy. The most effective training and validation accuracy for the VGG19-RNN architecture is 99% & 97.70%, respectively, and the loss was
0.02 & 0.09 at epoch 100.
