Phantom Image Classification via Hierarchical ML

The classification system is divided into multiple hierarchical levels (Level 0 to Level N), where each level performs a specific classification task. Level 0 performs initial modality classification, Level 1 performs phantom family classification, and subsequent levels perform increasingly specific classifications. This segmentation allows the complex classification task to be broken down into manageable stages, improving processing efficiency while maintaining system organization.

The patent introduces a hierarchical dimension to the classification process, transforming a potentially flat, monolithic classification system into a multi-level hierarchical structure. This dimensional transformation allows the system to handle complexity by distributing classification logic across multiple levels, where each level operates on a specific aspect of the image data, thereby improving overall productivity without overwhelming a single component.

The classification tree is segmented into hierarchical levels where Level 0 handles broad modality categories, Level 1 handles phantom families, and subsequent levels handle specific phantom types. This segmentation presents information to users in a structured, progressive manner rather than overwhelming them with all options simultaneously, thereby maintaining ease of operation while providing comprehensive phantom type coverage.

The system performs preliminary classification actions automatically through the hierarchical tree structure before presenting results to the user. The machine learning classifiers at each level pre-process and narrow down the classification options, so users receive refined results without having to manually navigate through all possible phantom types. This preliminary automated action reduces the operational burden on users while maintaining comprehensive adaptability.

The training process is segmented and distributed across multiple hierarchical levels. Each level's machine learning classifier is trained independently on specific features relevant to that classification level, rather than training a single complex classifier for all classifications. This segmentation of the training process reduces the complexity of individual training tasks while maintaining high classification accuracy through the cumulative effect of multiple specialized classifiers.

Each machine learning classifier in the hierarchical tree is optimized with local quality - trained on specific features and data relevant to its particular classification level. Level 0 classifiers are optimized for modality-specific features, Level 1 classifiers for phantom family characteristics, and so on. This local optimization allows each component to achieve high precision in its specific domain without requiring the entire system to be retrained, thereby managing training complexity while maintaining high overall measurement precision.