Performance, Trust, or both? COVID-19 Diagnosis and Prognosis using Deep Ensemble Transfer Learning on X-ray Imagesg±
ACM International Conference Proceeding Series
; 2022.
Artigo
em Inglês
| Scopus | ID: covidwho-20243833
ABSTRACT
The COVID-19 pandemic still affects most parts of the world today. Despite a lot of research on diagnosis, prognosis, and treatment, a big challenge today is the limited number of expert radiologists who provide diagnosis and prognosis on X-Ray images. Thus, to make the diagnosis of COVID-19 accessible and quicker, several researchers have proposed deep-learning-based Artificial Intelligence (AI) models. While most of these proposed machine and deep learning models work in theory, they may not find acceptance among the medical community for clinical use due to weak statistical validation. For this article, radiologists' views were considered to understand the correlation between the theoretical findings and real-life observations. The article explores Convolutional Neural Network (CNN) classification models to build a four-class viz. "COVID-19", "Lung Opacity", "Pneumonia", and "Normal"classifiers, which also provide the uncertainty measure associated with each class. The authors also employ various pre-processing techniques to enhance the X-Ray images for specific features. To address the issues of over-fitting while training, as well as to address the class imbalance problem in our dataset, we use Monte Carlo dropout and Focal Loss respectively. Finally, we provide a comparative analysis of the following classification models - ResNet-18, VGG-19, ResNet-152, MobileNet-V2, Inception-V3, and EfficientNet-V2, where we match the state-of-the-art results on the Open Benchmark Chest X-ray datasets, with a sensitivity of 0.9954, specificity of 0.9886, the precision of 0.9880, F1-score of 0.9851, accuracy of 0.9816, and receiver operating characteristic (ROC) of the area under the curve (AUC) of 0.9781 (ROC-AUC score). © 2022 ACM.
Chest X-ray; Classification; COVID-19; Deep Ensemble Transfer Learning; Pneumonia; SARS-CoV-2.; Classification (of information); Computer aided diagnosis; Convolutional neural networks; Deep learning; Image enhancement; Learning systems; Transfer learning; Areas under the curves; Classification models; Diagnosis and prognosis; Receiver operating characteristics; X-ray image; Coronavirus
Texto completo:
Disponível
Coleções:
Bases de dados de organismos internacionais
Base de dados:
Scopus
Tipo de estudo:
Estudo diagnóstico
/
Estudo observacional
/
Estudo prognóstico
Idioma:
Inglês
Revista:
ACM International Conference Proceeding Series
Ano de publicação:
2022
Tipo de documento:
Artigo
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