Lung Cancer Detection using Local Energy-Based Shape Histogram (LESH) Feature Extraction Using Adaboost Machine Learning Techniques
Sharvani1, Hemant K2

1Sharvani*, Student, P.G .Department of Electronics and Telecommunication, T.C.E.T, Mumbai, India.
2Hemant K, Associate Professor, Department of Electronics and Telecommunication, T.C.E.T, Mumbai, India.
Manuscript received on December 17, 2019. | Revised Manuscript received on December 21, 2019. | Manuscript published on January 10, 2020. | PP: 167-171 | Volume-9 Issue-3, January 2020. | Retrieval Number: B7671129219/2020©BEIESP | DOI: 10.35940/ijitee.B7671.019320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: It is difficult to find the exact symptoms of lung cancer due to the formation of the majority of cancer tissues in which the large tissue structure intersects differently. With digital images, this question can be evaluated. Images with the basic operation of the LESH Algorithm will be examined in this strategy. GLCM approach is used in this paper to pre-process the snap shots and feature extraction system and to check a patient’s disease rate at its it’s premature or unnatural to know it. The cancer stage will be assessed with the aid of the results . Using the data set and the cancer patient’s survival rate can be calculated. The conclusion is based entirely on the accurate and incorrect arrangement of tissue patterns. 
Keywords: Echo State Network (ESN), Clinical Decision Support Systems (CDSSs), Local Energy based Shape Histogram (LESH), Extreme Learning Machine (ELM), Echo State Network (ESN), AdaBoost, Support Vector Machine (SVM).
Scope of the Article: Learning Machine