A light-weight convolutional Neural Network Architecture for classification of COVID-19 chest X-Ray images

The COVID-19 pandemic has opened numerous challenges for scientists to use massive data to develop an automatic diagnostic tool for COVID-19. Since the outbreak in January 2020, COVID-19 has caused a substantial destructive impact on society and human life. Numerous studies have been conducted in search of a suitable solution to test COVID-19. Artificial intelligence (AI) based research is not behind in this race, and many AI-based models have been proposed. This paper proposes a lightweight convolutional neural network (CNN) model to classify COVID and Non_COVID patients by analyzing the hidden features in the X-Ray images. The model has been evaluated with different standard metrics to prove the reliability of the model. The model obtained 98.78%, 93.22%, and 92.7% accuracy in the training, validation, and testing phases. In addition, the model achieved 0.964 scores in the Area Under Curve (AUC) metric. We compared the model with four state-of-art pre-trained models (VGG16, InceptionV3, DenseNet121, and EfficientNetB6). The evaluation results demonstrate that the proposed CNN model is a candidate for an automatic diagnostic tool for the classification of COVID-19 patients using chest X-ray images. This research proposes a technique to classify COVID-19 patients and does not claim any medical diagnosis accuracy.