{'fraction_non_alphanumeric': 0.020202020202020204, 'fraction_numerical': 0.00808080808080808, 'mean_word_length': 5.270042194092827, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
X-Ray Image based COVID-19 Detection using Pre-trained Deep Learning Models

Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how pre-trained deep learning models can be adopted to perform COVID-19 detection using X-Ray images. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent image classification models. We highlight the challenges (including dataset size and quality) in utilising current publicly available COVID-19 datasets for developing useful deep learning models. We propose a semi-automated image pre-processing model to create a trustworthy image dataset for developing and testing deep learning models. The new approach is aimed to reduce unwanted noise from X-Ray images so that deep learning models can focus on detecting diseases with specific features from them. Next, we devise a deep learning experimental framework, where we utilise the processed dataset to perform comparative testing for several popular and widely available deep learning model families such as VGG, Inception, Xception, and Resnet. The experimental results highlight the suitability of these models for current available dataset and indicates that models with simpler networks such as VGG19 performs relatively better with up to 83% precision. This will provide a solid pathway for researchers and practitioners to develop improved models in the future.