Improved Lung Cancer Segmentation Using K-Means and Cuckoo Search
Paramjit Singh1, Pankaj Nanglia2, Aparna N Mahajan3
1Paramjit Singh*, Currently Pursuing Ph.D in Electrical & Electronics Engineering in Maharaja Agrasen University, Himachal Pradesh, India.
2Pankaj Nanglia, Currently Pursuing Ph.D in Electronics and Communication Engineering in Maharaja Agrasen University, Himachal, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on 23 November, 2019. | Manuscript published on December 10, 2019. | PP: 3746-3758 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6221129219/2019©BEIESP | DOI: 10.35940/ijitee.B6221.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Lung Cancer, Image Segmentation, K-mean Clustering, k-ABC, k-CS.
Scope of the Article: Clustering