Colposcope image recognition method for detecting cervical lesions

Abstract

The invention relates to the technical field of image recognition, in particular to a colposcope image recognition method for detecting cervical lesions. The method comprises the following steps: 1, collecting a sample image; 2, carrying out data preprocessing on the sample image; 3, establishing a sample feature space according to the preprocessed sample image; 4, constructing an optimal attribute predictor according to the sample feature space; and 5, classifying to-be-detected image samples according to the optimal attribute predictor. According to the technical scheme of the invention, themethod improves the attribute prediction accuracy and recall rate through adoption of the quasi-positive definite hypergraph regularization attribute learning method, can achieve the quick and accurate recognition and classification of the to-be-detected colposcope image through a learned attribute predictor, and achieves a good auxiliary detection and diagnosis effect.

Description

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AI Technical Summary

Benefits of technology

This invention improves how images are analyzed for better understanding compared with previous methods that only focused on specific areas or characteristics within them. It uses techniques like this to divide an entire picture into smaller parts called “pixels” instead of just one large area. These small regions represent different attributes such as color texture, shape, etc., but they still have their own unique properties when combined together. By performing these operations at various layers inside the pictures, we get clearer insights about what makes up our scene being captured differently from other scenes due to factors like light conditions. Overall, it provides improved results through enhanced analysis capabilities and increased resolution ability.

Problems solved by technology

This patents discusses various methods used during medical examinations on cervicitis (C), with different technical effects being discussed depending upon factors like patient risk or cost consideration. However, current techniques require complicated procedures such as histopathology, making it difficult to apply them effectively across large geographic regions where resources may lack.

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