Improved Lung Cancer Segmentation Using K-Means and Cuckoo Search

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Lung Cancer is among the deadliest disease that tolls mass every year. Information technology is playing an indispensable part in availing the most successful diagnosis and top treatment strategies to fight the condition when detected at an earliest stage. The work encompasses the improvement in the quality of image segmentation of Computer Tomography (CT) medical images of lung cancer. The paper evaluates the performance of two algorithms as a post segmentation process, namely, Artificial Bee Colony (ABC) and Cuckoo Search (CS). Support Vector Machine(SVM) is also used as a cross validator over the post segmentation algorithms ABC and CS. The experimental evaluation includes Accuracy, Precision, Error, Segmentation Time, Recall and F-measure to determine the success of the proposed hybrid model. The proposed results exhibit an improved Accuracy, Precision, Recall and F-measure by 5%, 6%, 3%, 4% and 10%, 11%., 12%, 11% for k-ABC and k-CS respectively.




Singh*, P., Nanglia, P., & Mahajan, D. A. N. (2019). Improved Lung Cancer Segmentation Using K-Means and Cuckoo Search. International Journal of Innovative Technology and Exploring Engineering, 9(2), 3746–3757.

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