Electronic laryngoscope medical image detection method based on local fine-grained contour features
By constructing an image segmentation network model and extracting fine-grained features from laryngoscope images, the problem of reliance on experience in electronic laryngoscope detection methods is solved, achieving more efficient and accurate organ contour recognition and anomaly detection.
CN115393326BActive Publication Date: 2026-01-09HEFEI UNIV OF TECH +1
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Patent Information
- Application Number
- CN202211042016.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-08-29
- Publication Date
- 2026-01-09
- Estimated Expiration
- 2042-08-29
AI Technical Summary
Technical Problem
Existing electronic laryngoscope image detection methods rely on doctors' experience, are prone to missed and false detections, are inefficient, and struggle to accurately identify organ outlines and mark abnormalities in complex backgrounds.
Method used
By acquiring and preprocessing laryngoscope images, an image segmentation network model is constructed to extract fine-grained features of organ contours. ResNet network is then used for classification to generate detection results.
Benefits of technology
It improves the accuracy of organ abnormality detection in complex contexts, reduces missed and false detections, and increases detection efficiency.
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Abstract
The application provides an electronic laryngoscope medical image detection method based on local fine-grained contour features, comprising: acquiring an electronic laryngoscope image, pre-processing the same to obtain a training data set, constructing a network model for image segmentation, training the same to obtain an organ segmentation model, inputting the training data set into the organ segmentation model to generate an organ segmentation data set, extracting a contour edge based on pixel point values of the organ segmentation data set to generate an organ contour data set, extracting fine-grained features based on tangent line inclination angles of each point of the organ edge in the organ contour data set, combining all the features into final features, and predicting a laryngoscope image category according to the final features. The electronic laryngoscope medical image detection method based on local fine-grained contour features can recognize and mark organ contours in a complex background, generate a detection result, and improve the accuracy of organ anomaly detection.
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