Urine Sediment Block Retrieval Using Decision Tree Voting
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Solution Overview
Problem
Current block retrieval methods for urine sediment images are inefficient due to the need for calculating complex Euclidean distances between feature vectors, leading to high computational requirements and imprecision in classification.
Innovation Solution
The method employs a plurality of decision trees with different structures and judgments, using block retrieval features to judge blocks and integrate results through voting, which reduces the need for complex parameter calculations and enhances precision by storing only indices in the leaf nodes, allowing for efficient and accurate retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Euclidean distance calculation is used for block retrieval, then retrieval accuracy is maintained, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the single complex Euclidean distance calculation into multiple simpler decision tree judgments. Each decision tree divides the feature space into regions using simple threshold comparisons, breaking down the complex retrieval problem into manageable segments that can be processed efficiently without sacrificing retrieval accuracy.
Solution Approach 2:
The patent replaces the mechanical calculation system (Euclidean distance computation) with a logical judgment system (decision trees). Instead of computing continuous distance values through mathematical formulas, the system uses discrete threshold-based judgments that are computationally simpler and faster while maintaining retrieval precision.
2Measurement precision
If multiple decision trees with different structures are used for block retrieval, then retrieval precision is enhanced through voting, but system complexity increases
Solution Approach 1:
The patent merges multiple decision trees into an integrated retrieval system where each tree contributes to the final result through voting. The individual decision trees are combined such that their collective judgment (through voting mechanisms) produces a more precise retrieval outcome than any single tree could achieve alone, while the overall system remains manageable through structured integration.
Data Source
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AI summary
The inventive concepts herein relate to performing block retrieval on a block to be processed of a urine sediment image. The method comprises: using a plurality of decision trees to perform block retrieval on the block to be processed, wherein each of the plurality of decision trees comprises a judgment node and a leaf node, and the judgment node judges the block to be processed to make it reach the leaf node by using a block retrieval feature in a block retrieval feature set to form a block retrieval result at the leaf node, and at least two decision trees in the plurality of decision trees are different in structures thereof and/or judgments performed by the judgment nodes thereof by using the block retrieval feature; and integrating the block retrieval results of the plurality of decision trees so as to form a final block retrieval result.