Fire-fly based MKFCM Segmentation and Hybrid Feature Extraction for Lung Cancer Detection
B. Mohamed Faize Basha1, M. Mohamed Surputheen2
1B. Mohamed Faize Basha, Research Scholar, Department of Computer Science, Jamal Mohamed College (Autonomous), [Affiliated to Bharathidasan University], Tiruchirappalli, Tamilnadu, India.
2Dr. M. Mohamed Surputheen, Associate Professor, Department of Computer Science, Jamal Mohamed College (Autonomous), [Affiliated to Bharathidasan University], Tiruchirappalli, Tamilnadu, India.

Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 7306-7312 | Volume-8 Issue-4, November 2019. | Retrieval Number: D5290118419/2019©BEIESP | DOI: 10.35940/ijrte.D5290.118419

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Abstract: The most serious and broad infections considered lung disease that sets up a principal general wellbeing risky and has a high demise level. In this worry, appropriate division of lung tumor from X-beam, CT output or, MRI is the moving stone to accomplishing totally electronic analysis framework for lung disease location. With the advancement of innovation and attainable quality of information, the regarded time of a radiologist can be secured by methods for PC apparatuses for tumor division. This paper, to improve the Lung cancer segmentation and classification a new model is introduce. To overawed the existing segmentation limitations in this proposed system for lung nodes detectionModified kernel-based Fuzzy c-means clustering (MKFCM) technique is used. The proposed method segmentation includes two modules, the fire-fly clustering module and the MKFCM clustering module. For feature Extraction feature of this paper a (Gray-Level Co-Occurrence Matrix), Local binary patterns (LBP) and Histogram of oriented gradients (HOG) based hybrid system is used. To select the best feature fire fly base Feature Selection (FS) technique is used. For proposed Lung cancer classification long short-term memory (LSTM) classifier is used. The proposed system is also named as FF-MKFCM-FF-FS-LSTM system. Finally the performances are evaluated. From that analysis the proposed module provide 96.55% of segmentation accuracy and the proposed classification provides 98.95% of classification accuracy.
Keywords: Computed Tomography, Modified Kernel-Based FCM, Fire-Fly Optimization, Segmentation, And Classification.
Scope of the Article: Classification.