Comput Math Methods Med - Ant colony optimization approaches to clustering of lung nodules from CT images.

Tópicos

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Resumo

Lung cancer is becoming a threat to mankind. Applying machine learning algorithms for detection and segmentation of irregular shaped lung nodules remains a remarkable milestone in CT scan image analysis research. In this paper, we apply ACO algorithm for lung nodule detection. We have compared the performance against three other algorithms, namely, Otsu algorithm, watershed algorithm, and global region based segmentation. In addition, we suggest a novel approach which involves variations of ACO, namely, refined ACO, logical ACO, and variant ACO. Variant ACO shows better reduction in false positives. In addition we propose black circular neighborhood approach to detect nodule centers from the edge detected image. Genetic algorithm based clustering is performed to cluster the nodules based on intensity, shape, and size. The performance of the overall approach is compared with hierarchical clustering to establish the improvisation in the proposed approach.

Resumo Limpo

lung cancer becom threat mankind appli machin learn algorithm detect segment irregular shape lung nodul remain remark mileston ct scan imag analysi research paper appli aco algorithm lung nodul detect compar perform three algorithm name otsu algorithm watersh algorithm global region base segment addit suggest novel approach involv variat aco name refin aco logic aco variant aco variant aco show better reduct fals posit addit propos black circular neighborhood approach detect nodul center edg detect imag genet algorithm base cluster perform cluster nodul base intens shape size perform overal approach compar hierarch cluster establish improvis propos approach

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