Enhancing Automated COVID Diagnosis from Chest X-rays using Convolutional Neural Networks and Transfer Learning

We propose an approach for the early detection of COVID-19 and other related lung diseases using artificial intelligence (AI) and deep learning-based methods. The proposed approach involves utilizing transfer learning over convolutional neural networks (CNNs) for the classification of chest X-ray images as normal or COVID-19 positive. To address the limited availability of X-ray images, we employed data augmentation techniques to expand the dataset size and enhance our model's performance on unseen data. We used variations of four pre-trained models: DenseNet, Inception, ResNet, and VGG, and found that DenseNet169 demonstrated the best performance with a validation accuracy of 100%.