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This research aims to improve the positioning accuracy of intelligent biopsy devices using machine learning models.

Research on Precise Positioning and Prediction of Intelligent Biopsy Devices Based on Machine Learning Models

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The HLOA-CNN-BIGRU algorithm improves localization accuracy in biopsies, suggesting better diagnostic outcomes using machine learning models.

This research aims to improve the positioning accuracy of intelligent biopsy devices using machine learning models.
Proposed the HLOA-CNN-BIGRU regression algorithm for precise localization of biopsy devices
Conducted correlation and violin graph analyses to assess performance
Performed comparative experiments with various machine learning algorithms
HLOA-CNN-BIGRU model outperformed other models in prediction accuracy
Mean square error of the model was 0.037 and root mean square error was 0.193, the lowest among tested models
Coefficient of determination reached 0.889, indicating strong data variation explanation

Wang et al. (2025) studied this question.

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View Full Paper

Ask AI

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Authors

The HLOA-CNN-BIGRU algorithm improves localization accuracy in biopsies, suggesting better diagnostic outcomes using machine learning models.

This research aims to improve the positioning accuracy of intelligent biopsy devices using machine learning models.
Proposed the HLOA-CNN-BIGRU regression algorithm for precise localization of biopsy devices
Conducted correlation and violin graph analyses to assess performance
Performed comparative experiments with various machine learning algorithms
HLOA-CNN-BIGRU model outperformed other models in prediction accuracy
Mean square error of the model was 0.037 and root mean square error was 0.193, the lowest among tested models
Coefficient of determination reached 0.889, indicating strong data variation explanation

Wang et al. (2025) studied this question.

View Full Paper

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