A fused lightweight CNN model for the diagnosis of COVID-19 using CT scan images

Abstract

Computed tomography is an effective tool that can be used for the fast diagnosis of COVID-19. However, in high case-load scenarios, there are chances of delay and human error in interpreting the scan images manually by an expert. An artificial intelligence (AI) based automated tool can be employed for fast and efficient diagnosis of this disease. For image-based diagnosis, convolutional neural networks (CNN) which is a subcategory of AI has been widely explored. However, these CNN models require significant computational resources for processing. Hence in this work, the performance of two lightweight least explored CNN models, namely SqueezeNet and ShuffleNet have been evaluated with CT scan images. While SqueezeNet produced an accuracy of 86.4%, ShuffleNet was able to provide an accuracy of 95.8%. Later, in order to improve the accuracy, a novel fused-model combining these two models has been developed and its performance has been evaluated. The fused-model outperformed the two base models with an overall accuracy of 97%. The analysis of the confusion matrix revealed an improved specificity of 96.08% and precision of 96.15% with a better fallout and false discovery rate of 3.91% and 3.84%, respectively.

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