Lung Cancer Identification and Prediction Based on VGG Architecture

Authors

  • T. Ambikadevi Amma Professor & Principal, Department of Computer Science and Engineering, Nehru College of Engineering and Research Centre, Pampady, India
  • Anu Rinny Sunny Assistant Professor, Department of Computer Science and Engineering, Nehru College of Engineering and Research Centre, Pampady, India
  • K. P. Biji Assistant Professor, Department of Computer Science and Engineering, Nehru College of Engineering and Research Centre, Pampady, India
  • Manisha Mohanan PG Scholar, Department of Computer Science and Engineering, Nehru College of Engineering and Research Centre, Pampady, India

Keywords:

Lung cancer, Deep learning, VGG architecture, Image classification

Abstract

Lung Cancer – the most fatal disease in human beings is the uncontrolled growth of abnormal cells in one or both the lungs. Lung Cancer is the leading cause of cancer death worldwide. People who smoke has the highest risk of cancer. It can also occur in people who have never even smoked too. The motive of this paper is to identify the probability and predict the possibility of cancerous and non-cancerous lung cancer. A deep learning approach – which has multilayered structure is applied for accurately identifying lung cancer. Deep Learning approaches focuses on Convolutional Neural Network(CNN) in order to identify cancer cells. CNN is a class of deep neural networks most commonly it is applied in order to analyze visual images VGG i.e. Visual Geometry Group based architecture with 16 layers is used for accurate identification also a computerized tomography images is used to classify the lung nodule and provides vital information about its severity.

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Published

15-07-2020

How to Cite

[1]
T. A. . Amma, A. R. . Sunny, K. P. . Biji, and M. . Mohanan, “Lung Cancer Identification and Prediction Based on VGG Architecture”, IJRESM, vol. 3, no. 7, pp. 88–92, Jul. 2020.

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Articles