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DGCNN: deep convolutional generative adversarial network based convolutional neural network for diagnosis of COVID-19
1Computer Science and Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh India.
Multimedia Tools and Applications
|April 18, 2022
Summary
A new deep learning model, DGCNN, aids in diagnosing COVID-19 from chest X-rays when RT-PCR tests are insufficient. This method improves detection accuracy for coronavirus disease 2019 (COVID-19) cases.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Infectious Diseases
Background:
- The COVID-19 pandemic poses a significant global health risk.
- Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the primary diagnostic method but has limitations in sensitivity.
- Limited availability of COVID-19 chest X-ray (CXR) datasets due to patient privacy concerns hinders research.
Purpose of the Study:
- To develop an efficient deep learning model for diagnosing COVID-19.
- To address the limitations of existing data augmentation techniques for CXR datasets.
- To improve the accuracy of COVID-19 diagnosis using medical imaging.
Main Methods:
- A deep convolutional generative adversarial network (DGAN) was designed to generate realistic CXR images.
- A convolutional neural network (CNN) was employed for image classification.
- The DGAN and CNN were combined into a DGCNN model for COVID-19 diagnosis.
Main Results:
- Extensive experiments were conducted to evaluate the DGCNN model's performance.
- The proposed DGCNN model demonstrated significant improvements in diagnostic accuracy.
- The model effectively diagnoses COVID-19 suspected subjects using CXR images.
Conclusions:
- The DGCNN model offers an efficient and accurate approach for COVID-19 diagnosis.
- This deep learning approach can supplement existing diagnostic methods like RT-PCR.
- The DGCNN model shows promise in enhancing the control of the COVID-19 outbreak through improved diagnosis.
