Convolutional neural networks for imbalanced chest X-ray images classification
3rd International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2022
; 12509, 2023.
Article
in English
| Scopus | ID: covidwho-2234617
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. © 2023 SPIE.
chest X-ray; convolutional neural networks; focal loss; image classification; image enhancement; Classification (of information); Convolution; Health risks; Medical imaging; Automated diagnosis; Chest X-ray image; Convolutional neural network; Health care providers; Human health; Images classification; Loss functions; X-ray image classifications
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Randomized controlled trials
Language:
English
Journal:
3rd International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2022
Year:
2023
Document Type:
Article
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