DenSplitnet: Classifier-invariant neural network method to detect COVID-19 in chest CT data

Abstract

Objective:COVID-19 has made an unprecedented impact on humanity. The Healthcare sector, in an effort to curb COVID-19, could leverage Artificial Intelligence (AI) to its aid, especially in diagnosing it through the classification of Chest-CT scans. However, data scarcity plagues the medical domain. Therefore, any AI solution must be capable of learning from limited data. Also the resulting AI could relieve radiologists from exhaustion and aid medical practitioners as a valuable diagnostic tool. Methods:Our proposed model DenSplitnet uses Dense blocks to learn image features, Self-Supervised Learning for pre-training to learn the context, and a novel two-way split branch at the final classification layer for classifier-invariant generalization ability. Results:DenSplitnet achieves state-of-the-art performance on four benchmark chest-CT scan datasets for COVID-19. The model performs well on the Sars-cov-2 ct-scan dataset. The Test accuracy is 91.92%, F1-Score is 91.30%, Precision is 96.55%, and Recall is 87.04%. The model achieves test accuracy of 86.17%, Precision of 82.94%, Recall of 83.72%, and F1-Score of 83.23% on the Sars-cov-2 ct-scan multiclass dataset. The model obtains a test accuracy of 86.21%, an F1-Score of 83.91%, and an AUC of 0.95 in the Covid-ct-dataset. The model obtains a test accuracy that is 73.11% and an F1-Score of 70.97% in the TransferLearn-ct dataset. Conclusion:The findings support the practical usability of the DenSplitnet as a technological AI assistance to radiologists and other medical professionals, alongside Grad-CAM plots for explainable AI and the theoretical examination of the classifier-invariant generalization capacity of the network. The healthcare sector can benefit from this technology in a number of ways.