Analysis and detection of COVID-19 cases on chest X-ray images using a novel architecture self-development deep-learning

Abstract

The recent ongoing pandemic coronavirus disease 2019 (COVID-19) is growing increasingly out of control globally, posing a severe threat to human health. The use of artificial intelligence (AI) in predicting COVID-19-positive individuals becomes a promising tool that may enhance the existing diagnosis modality. Algorithms in supporting classifications for chest X-ray images face challenges in terms of dependability. With this aim, a convolutional neural network (CNN) model FASNet is proposed to identify chest X-ray images of three distinct conditions: pneumonia, COVID-19, and normal (or healthy) cases. The FASNet model consists of four convolution layers and two fully connected layers. The pre-trained deep learning models were used and included in our self-development FASNet CNN model. In the first convolutional layer, the size of the kernel $1 \times 1$ is used on each pixel as a fully connected connection with the aim of reducing the channel depth and number of parameters of the model. The early-stopping class and dropout layer are used to limit the number of neural connections and prevent overfitting. The dataset for this study was derived from an open-source collection of 6,432 images for training and testing. As the result, our approach successfully detected COVID-19 infected individuals, pneumonia, and healthy ones with a 98.48% accuracy. This promising preliminary results lead us to expect that the FASNet model can be used in further development research to assist in diagnosing COVID-19. The result with FASNet model has a high correlation in comparison with other popular models such as ResNet50V2 and MobileNetV2.

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