Li P, Wang X, Liu P, Xu T, Sun P, Dong B, Xue H. Cervical Lesion Classification Method Based on Cross-Validation Decision Fusion Method of Vision Transformer and DenseNet.
JOURNAL OF HEALTHCARE ENGINEERING 2022;
2022:3241422. [PMID:
35607393 PMCID:
PMC9124126 DOI:
10.1155/2022/3241422]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 04/24/2022] [Accepted: 04/28/2022] [Indexed: 11/17/2022]
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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