Research on pulmonary nodule recognition algorithm based on micro-variation amplification
- VernacularTitle:基于微变放大技术的肺结节识别算法研究
- Author:
Zirui ZHANG
1
;
Zichen JIAO
2
;
Xiaoming SHI
1
;
Tao WANG
3
Author Information
1. c Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, P. R. China
2. Department of Thoracic Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008, P. R. China
3. 1. Department of Thoracic Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, P. R. China 2. Department of Thoracic Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008, P. R. China
- Publication Type:Journal Article
- Keywords:
Pulmonary nodules;
preoperative localization;
micro-variation amplification;
computer vision;
multiscale detection
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2025;32(03):339-344
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop an innovative recognition algorithm that aids physicians in the identification of pulmonary nodules. Methods Patients with pulmonary nodules who underwent thoracoscopic surgery at the Department of Thoracic Surgery, Affiliated Drum Tower Hospital of Nanjing University Medical School in December 2023, were enrolled in the study. Chest surface exploration data were collected at a rate of 60 frames per second and a resolution of 1 920×1 080. Frame images were saved at regular intervals for subsequent block processing. An algorithm database for lung nodule recognition was developed using the collected data. Results A total of 16 patients were enrolled, including 9 males and 7 females, with an average age of (54.9±14.9) years. In the optimized multi-topology convolutional network model, the test results demonstrated an accuracy rate of 94.39% for recognition tasks. Furthermore, the integration of micro-variation amplification technology into the convolutional network model enhanced the accuracy of lung nodule identification to 96.90%. A comprehensive evaluation of the performance of these two models yielded an overall recognition accuracy of 95.59%. Based on these findings, we conclude that the proposed network model is well-suited for the task of lung nodule recognition, with the convolutional network incorporating micro-variation amplification technology exhibiting superior accuracy. Conclusion Compared to traditional methods, our proposed technique significantly enhances the accuracy of lung nodule identification and localization, aiding surgeons in locating lung nodules during thoracoscopic surgery.
