International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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COVID-19: Improving the accuracy using data augmentation and pre-trained DCNN Models

  • Saif Hassan (Center of Excellence for Robotics, Artificial Intelligence, and Blockchain, Department of Computer Science, Sukkur IBA University) ;
  • Abdul Ghafoor (Center of Excellence for Robotics, Artificial Intelligence, and Blockchain, Department of Computer Science, Sukkur IBA University) ;
  • Zahid Hussain Khand (Center of Excellence for Robotics, Artificial Intelligence, and Blockchain, Department of Computer Science, Sukkur IBA University) ;
  • Zafar Ali (Center of Excellence for Robotics, Artificial Intelligence, and Blockchain, Department of Computer Science, Sukkur IBA University) ;
  • Ghulam Mujtaba (Center of Excellence for Robotics, Artificial Intelligence, and Blockchain, Department of Computer Science, Sukkur IBA University) ;
  • Sajid Khan (Center of Excellence for Robotics, Artificial Intelligence, and Blockchain, Department of Computer Science, Sukkur IBA University)
  • 투고 : 2024.07.05
  • 발행 : 2024.07.30
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초록

Since the World Health Organization (WHO) has declared COVID-19 as pandemic, many researchers have started working on developing vaccine and developing AI systems to detect COVID-19 patient using Chest X-ray images. The purpose of this work is to improve the performance of pre-trained Deep convolution neural nets (DCNNs) on Chest X-ray images dataset specially COVID-19 which is developed by collecting from different sources such as GitHub, Kaggle. To improve the performance of Deep CNNs, data augmentation is used in this study. The COVID-19 dataset collected from GitHub was containing 257 images while the other two classes normal and pneumonia were having more than 500 images each class. There were two issues whike training DCNN model on this dataset, one is unbalanced and second is the data is very less. In order to handle these both issues, we performed data augmentation such as rotation, flipping to increase and balance the dataset. After data augmentation each class contains 510 images. Results show that augmentation on Chest X-ray images helps in improving accuracy. The accuracy before and after augmentation produced by our proposed architecture is 96.8% and 98.4% respectively.

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참고문헌

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