Özet:

This paper investigates an efficient image segmentation method for medical imaging with the goal of reducing clinicians' interpretation of computer tomography (CT) scan images. Large pictures generated by modern medical imaging modalities are particularly difficult to manually evaluate. The results of segmentation algorithms are determined by their precision and convergence time. There is a pressing need to investigate and develop novel evolutionary algorithms to overcome the challenges related with medical picture segmentation at the moment. Lung cancer is the most often diagnosed cancer in males all over the world. Early identification of lung cancer leads to appropriate treatment, which saves lives. An Effective Wrapper FS Based on Binary Swallow Swarm Optimization with Score-Based Criteria Fusion for Image Segmentation and Feature Selection scheme is proposed in this article. The experimental outcome of the proposed scheme is compared with existing approaches and the proposed lung cancer detection scheme outperforms the existing approaches.

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