The document describes a lung cancer detection system that uses CT scans. It discusses (1) segmenting the lungs from CT images using adaptive thresholding and connected component analysis, (2) detecting nodule candidate regions using multi-thresholding and rule-based pruning, and (3) optimizing the rule-based pruning using a genetic algorithm trained fuzzy inference system to reduce false positives while maintaining high sensitivity. Experimental results on a publicly available lung image database show the optimized fuzzy system achieved better performance than a conventional rule-based approach.