Convolutional neural networks for imbalanced chest x-ray images classification

Ouyang Qi

doi:10.1117/12.2656005

12 January 2023 Convolutional neural networks for imbalanced chest x-ray images classification

Ouyang Qi

Proceedings Volume 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022); 125092J (2023) https://doi.org/10.1117/12.2656005
Event: Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 2022, Guangzhou, China

Abstract

The COVID-19 epidemic has spread throughout the world and poses a serious threat to human health. Any technical device that provides the accurate and rapid automated diagnosis of COVID-19 can be extremely beneficial to healthcare providers. A new workflow for performing automated diagnosis is proposed in this paper. The proposed methods are built on a well-designed framework, two kinds of CNN architectures including a custom CNN and a pre-trained CNN are utilized to verify the effectiveness of the focal loss function. According to the experimental findings, both CNNs that were enhanced with the focal loss function converged faster and achieved higher accuracy on the test set, outperformed the models that utilized cross-entropy loss that does not consider the class-imbalanced issue in the multi-class image classification with imbalanced Chest X-ray (CXR) image datasets. In addition, image enhancement techniques turned out to be very helpful for enhancing the CXR image signatures to achieve better performance in our work.

Citation Download Citation

Ouyang Qi "Convolutional neural networks for imbalanced chest x-ray images classification", Proc. SPIE 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 125092J (12 January 2023); https://doi.org/10.1117/12.2656005

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