Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset (2004.01184v1)

Abstract: The COVID-19 coronavirus is one of the devastating viruses according to the world health organization. This novel virus leads to pneumonia, which is an infection that inflames the lungs' air sacs of a human. One of the methods to detect those inflames is by using x-rays for the chest. In this paper, a pneumonia chest x-ray detection based on generative adversarial networks (GAN) with a fine-tuned deep transfer learning for a limited dataset will be presented. The use of GAN positively affects the proposed model robustness and made it immune to the overfitting problem and helps in generating more images from the dataset. The dataset used in this research consists of 5863 X-ray images with two categories: Normal and Pneumonia. This research uses only 10% of the dataset for training data and generates 90% of images using GAN to prove the efficiency of the proposed model. Through the paper, AlexNet, GoogLeNet, Squeeznet, and Resnet18 are selected as deep transfer learning models to detect the pneumonia from chest x-rays. Those models are selected based on their small number of layers on their architectures, which will reflect in reducing the complexity of the models and the consumed memory and time. Using a combination of GAN and deep transfer models proved it is efficiency according to testing accuracy measurement. The research concludes that the Resnet18 is the most appropriate deep transfer model according to testing accuracy measurement and achieved 99% with the other performance metrics such as precision, recall, and F1 score while using GAN as an image augmenter. Finally, a comparison result was carried out at the end of the research with related work which used the same dataset except that this research used only 10% of original dataset. The presented work achieved a superior result than the related work in terms of testing accuracy.

• The paper demonstrates that GAN-driven augmentation, using only 10% real chest X-ray data, effectively boosts training for COVID-19 pneumonia detection.
• It employs multiple transfer learning models, with ResNet18 achieving 99% accuracy, outperforming traditional data augmentation techniques.
• The study highlights the potential for synthetic imaging to enhance medical diagnostics in resource-limited settings by reducing overfitting and data scarcity.

Detection of COVID-19 Associated Pneumonia Using GANs and Deep Transfer Learning

The paper "Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset" by Khalifa et al., presents a sophisticated approach to the detection of COVID-19-associated pneumonia using chest X-ray images. It addresses a critical challenge in medical imaging, particularly the scarcity of annotated data, which often leads to overfitting in deep learning models.

The research capitalizes on Generative Adversarial Networks (GANs) alongside deep transfer learning to overcome conventional limitations and achieve high accuracy in classifying X-rays into normal and pneumonia-infected categories. The dataset consists of 5,863 X-ray images, curated into two distinct classes. Notably, only 10% of this dataset was utilized as real training data, with GANs generating the remaining 90% to boost the dataset size and ensure model robustness.

The experimental results underscore the efficacy of the GAN-augmented model architecture, with ResNet18 emerging as the most effective model, achieving a testing accuracy of 99%. This exceeds the performance of similar studies using the same dataset but employing traditional data augmentation methods and more extensive training datasets. For comparative purposes, the ResNet18 model surpassed previous models such as a Convolutional Neural Network used by Kermany et al., which reached 92.80% accuracy.

Model Architecture and Methodology

The methodology consists of three phases:

1. Preprocessing Phase: This involved image augmentation using GANs — a critical step in generating additional training data and mitigating overfitting. The GAN utilized was tailored with multiple convolutional and activation layers to generate realistic synthetic images from noise inputs.
2. Training Phase: Training incorporated several deep transfer learning models. The choice of models like AlexNet, GoogLeNet, SqueezeNet, and ResNet18 was strategic, focusing on architectures with fewer layers to reduce computational complexity and resource consumption. The training strategy divided the dataset into 80% for training and 20% for testing.
3. Testing Phase: Evaluations were conducted using performance metrics such as precision, recall, and F1 score. ResNet18 outperformed other architectures across all performance metrics, underscoring its optimality in this specific application.

Key Findings and Implications

The paper succeeds in showing that GANs can significantly alleviate the challenges of limited training data in medical imaging. The augmentation process not only increased dataset size but enhanced model versatility and accuracy, as demonstrated by ResNet18's superior performance.

From a practical standpoint, integrating GAN-generated data with transfer learning models has profound implications for medical diagnostics, particularly in resource-constrained settings. The approach could be adapted for other medical image classification tasks, suggesting a broad horizon for further research.

Future Directions

The framework outlined in this paper could catalyze future studies exploring deeper integration of GANs in medical imaging, potentially expanding to 3D imaging modalities or real-time applications. Additionally, investigations into optimizing GAN architectures for specific medical conditions or data types could refine these methods, increasing model adaptability across diverse clinical scenarios.

Overall, Khalifa et al. offer a compelling strategy that not only enhances pneumonia detection in COVID-19 patients using deep learning but contributes meaningfully to the growing field of AI-enhanced medical diagnostics. The promising results suggest a valuable paradigm shift, leveraging synthetic data to achieve advanced clinical outcomes in the field of machine learning.