A Hierarchical Decision Engine for Computer Aided Lung Nodule Detection from CT Images

We present a novel hierarchical modular decision engine for lung nodule detection from CT images implemented by Artificial Neural Networks. The proposed Computer Aided Detection (CAD) technique encompasses several desirable properties such as mimicking physicians by means of geometric multi-perspective analysis, computational efficiency, and most importantly archiving high performance in detection accuracy. One advantage of this decision engine is that it supports the combination of spatial-level and feature-level information analysis in an efficient way. Our methodology overcomes some of the limitations of current lung nodule detection techniques by combining geometric multi-perspective analysis with global and local feature analysis. The proposed modular decision engine design is flexible to modifications in the decision modules; the engine structure can adopt the modifications without re-designing the entire system. The engine can easily accommodate multi-learning scheme and parallel implementation so that each information type can be processed (in parallel) by the most adequate learning technique of its own.