AI Article Synopsis
- The paper introduces a fusion method combining Vision Transformer and DenseNet161 models to enhance clinical diagnosis accuracy.
- The methodology involves processing acetic acid images, training the models using fivefold cross-validation, and averaging the predictions to determine the most likely classification.
- The proposed method achieved a 68% accuracy rate for identifying cervical lesions, reducing missed detections and improving patient health outcomes.
Article Abstract
Objective: In order to better adapt to clinical applications, this paper proposes a cross-validation decision-making fusion method of Vision Transformer and DenseNet161.
Methods: The dataset is the most critical acetic acid image for clinical diagnosis, and the SR areas are processed by a specific method. Then, the Vision Transformer and DenseNet161 models are trained by the fivefold cross-validation method, and the fivefold prediction results corresponding to the two models are fused by different weights. Finally, the five fused results are averaged to obtain the category with the highest probability.
Results: The results show that the fusion method in this paper reaches an accuracy rate of 68% for the four classifications of cervical lesions.
Conclusions: It is more suitable for clinical environments, effectively reducing the missed detection rate and ensuring the life and health of patients.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9124126 | PMC |
| http://dx.doi.org/10.1155/2022/3241422 | DOI Listing |
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