| Autor: |
Kim Anh Phung, Thuan Trong Nguyen, Nileshkumar Wangad, Samah Baraheem, Nguyen D. Vo, Khang Nguyen |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Journal of Imaging, Vol 8, Iss 12, p 323 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2313-433X |
| DOI: |
10.3390/jimaging8120323 |
| Popis: |
The application of chest X-ray imaging for early disease screening is attracting interest from the computer vision and deep learning community. To date, various deep learning models have been applied in X-ray image analysis. However, models perform inconsistently depending on the dataset. In this paper, we consider each individual model as a medical doctor. We then propose a doctor consultation-inspired method that fuses multiple models. In particular, we consider both early and late fusion mechanisms for consultation. The early fusion mechanism combines the deep learned features from multiple models, whereas the late fusion method combines the confidence scores of all individual models. Experiments on two X-ray imaging datasets demonstrate the superiority of the proposed method relative to baseline. The experimental results also show that early consultation consistently outperforms the late consultation mechanism in both benchmark datasets. In particular, the early doctor consultation-inspired model outperforms all individual models by a large margin, i.e., 3.03 and 1.86 in terms of accuracy in the UIT COVID-19 and chest X-ray datasets, respectively. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
