Computer System for Perception of Lung Cancer
Rakhi Nautiyal1, Anil Dahiya2, Priyanka Dahiya3
1Rakhi Nautiyal, Department of IT, DIT University, (Uttarakhand), India.
2Anil Dahiya, Department of IT, DIT University, (Uttarakhand), India.
3Priyanka Dahiya, Department of IT, DIT University, (Uttarakhand), India.
Manuscript received on 10 October 2019 | Revised Manuscript received on 24 October 2019 | Manuscript Published on 26 December 2019 | PP: 868-872 | Volume-8 Issue-12S October 2019 | Retrieval Number: L119410812S19/2019©BEIESP | DOI: 10.35940/ijitee.L1194.10812S19
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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: Detection of cancer in its previous stages to increases survival rate of patient. CAD system is efficient because it take minimum time to detect weather the patient has cancer or not. It is very difficult for detection of lung cancer its earlier stage as it takes many tests. There are many of the CAD system which is designed for earlier detection of tumors. Many CAD systems have been designed in past for early detection of lung tumor. For segmentation purpose Thresholding is used and detection of area in which suspected tumor part growing algorithms is used. There are various factor is calculated using GLCM. Multilayer feed forward BPNN approaches for classify the feature set. Performance is calculated in form of mean square error (MSE) using BPNN. The CAD (computer aided diagnosis) model gives 90% true count. For implementation purpose MATLAB is used.
Keywords: CAD, Back Propagation Neural Network, Region of Interest, Computed Tomography.
Scope of the Article: Computer Network